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PRAUTOCAL corpus: a corpus for the study of Down syndrome prosodic aspects

Oral productions of speakers with Down syndrome exhibit special characteristics that have been the target of study for decades. In spite of this attention, the availability of rich resources for its analysis is still scarce. In this paper, we present the definition and compiling procedure of a corpus of semi-controlled oral productions of speakers with Down syndrome that aims to allow the analysis of how these speakers with these speakers produce functional and linguistic aspects of speech. The PRAUTOCAL corpus has been recorded while using a video game for training oral competences. Utterances are related to well defined communicative tasks recorded by both speakers with Down syndrome and typically developing speakers. We present the procedure for human experts to evaluate the recordings and the transcription criteria followed for enriching the utterances of the corpus. PRAUTOCAL permits the analysis of the clear contrast in voice and speech between individuals with Down syndrome and typically developing speakers, taking into account the high heterogeneity of the speech problems characteristic of the syndrome. This material allows the analysis of the speech problems in Down syndrome, with applications to the generation of knowledge that could be used in future works for therapists to prepare specific training or enriching diagnosis regarding possible speech and language disorders.


Introduction
In all circumstances, the process of collecting a spoken corpus is difficult, time consuming and expensive. However, compiling a corpus of controlled samples of speech of individuals with Down syndrome (DS) poses extra challenges, due to the special characteristics of the informants: among others, they can suffer developmental language problems (Martin et al., 2009), attention impairments (Martínez et al., 2011) and/or short-term memory problems (Chapman & Hesketh, 2001). In particular, the latter two make it difficult for speakers with the syndrome to follow instructions and focus on speech tasks during medium length and long recording sessions. The lack of a reference corpus of comparable speech of typically developing (TD) (i.e. without disabilities) individuals also hampers the study of the speech and language characteristics of this population. Nevertheless, there is a growing need for training data to develop automated tools -specifically designed for individuals with special needs-that use spoken language technology: automatic speech recognition, computer-aided speech therapy tools, learning tools. In all these cases, the underlying machine learning models and the use of deep learning technologies requires large quantities of data. In this paper, we show how, as an alternative to classical recording corpus procedures, using a video game is a strategy that permits speakers' fatigue and loss of attention to be reduced. Over different recording campaigns, we have collected the corpus PRAUTOCAL described in the following sections.
PRAUTOCAL is a corpus of northern/central peninsular Spanish speakers with DS that has an important volume of transcribed and annotated recordings, as well as quality assessments and references of TD users for comparison. We expect the availability of PRAUTOCAL will favour the analysis of specific aspects of the speech of individuals with DS, thus generating a body of knowledge that will allow therapists to arrange specific speech and language training in future applications and/or enrich the diagnosis of possible speech and language disorders.
Our video game was designed to train oral competences related to prosody and pragmatics in a realistic context for the Spanish language. It includes preestablished oral production activities that take into account such specific linguistic and communicative functions as learning objectives (see Sect. 3.1). This fact contributes to the creation of a corpus with utterances classified in terms of prosodic function, production mode (read, elicited, imitation or spontaneous speech) and linguistic politeness (see Sect. 3.2). The fundamental idea for promoting the efficient training of players during game sessions is that users must express themselves correctly to keep on playing. Every time the user does not utter appropriately, he or she must repeat the activity. This fact permits PRAUTOCAL to be annotated in terms of quality. In previous works, we set the quality assessment information with off-line evaluations (Corrales-Astorgano et al., 2019). In this paper, we add subjective evaluations following a rubric that includes information about fluency, intonation curve, speech rate, intelligibility and correctness and word omissions. Subjective judgments of quality are a valuable source of information, especially when they come from speech therapists. In PRAUTOCAL, this information is complemented with a reference concerning goodness of pronunciation provided by recordings of the same sentences uttered by TD speakers.
To put our work in context, the paper first presents other available corpora related to the speech of people with DS in Sect. 2, in order to highlight the importance of this resource. Section 3.1 describes the video game we used for compiling the corpus. The production activities that focus on prosody are detailed in Sect. 3.2. Section 3.3 sets out the informant profiles and Sect. 3.4 describes the recording campaigns. Sections 4 and 5 are devoted to presenting concerns related to the evaluation of oral turns, both with manual and automatic methods. In Sect. 6, we discuss the potential use of PRAUTOCAL for analyzing the differences between TD speakers and DS speakers, human based scores and the impact on the quality of the production mode. Section 7 refers to distribution concerns. We end the paper with the conclusions in Sect. 8 to highlight the importance of a resource that includes different evaluations of the oral productions of speakers with DS, taking into account different aspects related to prosody, thus allowing a comparison with productions of TD speakers.

State of the art
There are studies about the impairments in the spoken language of people with DS that include the recording of a corpus to experimentally study various aspects of the linguistic domains, although these corpora are not publicly available [for a review see Kent and Vorperian (2013)]. The aims, theoretical background and methodology of each study determine the features of the recorded corpus: content, number of speakers, demographics of the speakers, etc. Broadly speaking, there are works that focused on acoustic analysis (Albertini et al., 2010;Rochet-Capellan & Dohen, 2015;Bunton & Leddy, 2011;Seifpanahi et al., 2011;O'Leary et al., 2020), voice quality (Rodger 2009;Lee et al., 2009), prosodic skills (Zampini et al., 2016), speech disfluencies (Eggers & Van Eerdenbrugh, 2017), speech intelligibility (Wild et al., 2018), consonant, vowel and word duration (Brown-Sweeney & Smith, 1997) and perceptual evaluations (Moura et al., 2008;O'Leary et al., 2020). There are also corpora built specifically for a task: the Alborada-I3A corpus (Saz et al., 2010), designed for research in speech technologies, and the AD-Child.Ru corpus (Lyakso et al., 2019), created for studies of speech development.
Other disorders and pathologies that also affect spoken language and communication skills, such as dysarthria, aphasia, autism, Parkinson's disease, dementia, Alzheimer's disease, etc, have also been investigated and spoken corpora have been collected. The reference corpora for dysarthric speech in American English include Nemours (Menendez-Pidal et al., 1996), Universal Access (Kim et al., 2008) and TORGO (Rudzicz et al., 2012). There are also corpora in other languages, such as Korean (Kim et al., 2016) and French (Fougeron et al., 2010;Meunier et al., 2016). From an applied approach, projects on speech technology recorded a corpus of dysarthric speakers operating their home appliances using voice commands (Nicolao et al., 2016;Gemmeke et al., 2013). AphasiaBank (Forbes et al., 2012;MacWhinney et al., 2011) is a reference corpus for the study of aphasia. Other corpora of aphasic speech are designed for automatic speech intelligibility assessment (Le et al., 2016), and contain interactions with a tablet application designed for therapeutic purposes. As far as the autism spectrum disorder is concerned, speech corpora based on recording the sessions of the Autism Diagnostic Observation Schedule (ADOS) have been collected (Lahiri et al., 2020;Li et al., 2019;Lin et al., 2018), an instrument for the diagnosis and assessment of autism. There are also corpora recorded with the aim of identifying and assessing the severity of the speech disorder in patients with Parkinson's disease (Hauptman et al., 2019;Khan et al., 2020). Several databases for studying the speech of patients with dementia are available. DementiaBank contains the recordings from English speakers with Alzheimer's disease during a picture description task (Becker et al., 1994) in a 5 year longitudinal study. ILSE is a German database that includes participants with age-associated cognitive impairment and Alzheimer's disease, recorded over the course of 20 years (Weiner et al., 2016). The Hungarian MCI-mAD database contains recordings of speakers with cognitive impairment and Alzheimer's disease (Gosztolya et al., 2019). A corpus of French patients with dementia was recorded to study the automatic assessment of dementia (Satt et al., 2014).
In this context, the use of speech enabled games can help in the recording process, motivating the participant speakers. For instance, Voice Race is an online educational game that has been used to collect an English corpus of over 55,000 utterances (18.7 h of speech) (McGraw et al., 2009). It is based on the use of flashcards, and players earn points by using speech to match terms with their definitions. Another example is the quiz game that was used to collect 18,300 utterances (3.87 h of speech) of European Portuguese speech over the Internet (Freitas et al., 2010). Finally, a web enabled multimodal language game has been used to collect an English-L2 speech corpus produced by Swiss German-L1 students. The recorded corpus contains 814 utterances in which the subjects had conversations with an animated agent, obtaining points and badges (Baur et al., 2014). In view of the goodness of using speech enabled games to collect speech samples, we used the same approach for compiling a corpus of speech in DS.

The learning video game
As detailed in González-Ferreras et al. (2017) and Aguilar (2019), the video game has the structure of a graphic adventure game, providing conversations with characters and navigation through scenarios. The video game includes three types of activities: comprehension, production and visual cognitive activities. Comprehension activities are focused on lexical-semantic comprehension and the improvement of prosodic perception in specific contexts. In these activities, players must choose between different options to continue a conversation with a game character. Production activities are aimed at oral production, so the player is encouraged by the game to train his/her speech, keeping in mind such prosodic aspects as intonation, expression of emotions or syllabic emphasis (see Sect. 3.2.1). In these activities, the player engages in conversation with the characters and must choose between different options to continue the dialogue or to record some sentences (see Fig. 1). These activities are the source of the oral corpus that we describe in this paper. Finally, the visual activities are included to add variety to the game and to practice other skills not directly related with language, such as attention or visual perception skills.
Information about the user interaction and the audio recordings of the production activities are stored automatically by the video game during the game sessions. This information can be used to analyze the user's evolution in successive game sessions and the audio recordings increase the speech corpus. This user interaction log has information about the game time, the attempts to complete a task, the number of mouse clicks or the assistance given to the user. As a result of the evaluation of the proficiency of the user performance during the different activities, the player is allowed to continue playing or forced to repeat the current activity. A maximum number of attempts was fixed at three to avoid frustration in the players. In addition, to reduce the ambient noise in the recording process, the players used a headset with microphone incorporated (Plantronics USB headset, recording MS-WAVE PCM audio with frame rate 44100, 16 bits per sample and mono).
We used three different versions of the same video game to compile the corpus: the source version, called La piedra mágica, whose architecture is described in González-Ferreras et al. (2017); a more elaborated and complete release called PRADIA (Aguilar, 2019), with the same game narrative, but with an increased number of games and tasks; and a simplified version that includes only the production activities that were used to collect audio samples from TD users. The current version of PRADIA can be used in three modalities, depending on how the quality of the spoken answers is evaluated to allow the player to continue: automatic decision, three-level human based decision or template-based human based decision (detailed in Sect. 4.1.2).
It's okay. I will be happy to help you.)

Speech production activities
The prosodic categories follow the framework of intonative phonology, which takes into account the difference between linguistic and paralinguistic categories in the prosody component. From this approach, we assume the existence of prosodic categories (prominence, organization of sentences in prosodic groups and intonation), which can be modified in their phonetic implementation depending on paralinguistic cues (e.g., emotions) (Ladd, 2008). Therefore, prosody is conceived as a phenomenon of both form and meaning, and prosodic differences, regardless of their systematic, conventional or natural nature, affect the processes of meaning interpretation. In what refers to linguistic functions, we follow Halliday's categories (Halliday, 1970), according to the particular paralinguistic situation in which the oral turn of the user is produced. As detailed in Sect. 3.2.3, the production mode can change as the video game adapts to the speakers' difficulties when they have to repeat the activities. Both prosodic and linguistic functions are a matter of interest in the study of DS language development. Stojanovik (2011) and Loveall et al. (2021) showed that DS speakers present deficits in prosodic production affecting focus, chunking and turnend. Concerning linguistic functions, Abbeduto et al. (2007); Abbeduto (2008) report how DS speakers display areas of substantial pragmatic weakness, such as rendering descriptions and interacting. Martin et al. (2009) highlight the fact that expressive language skills are more impaired than receptive skills in young individuals with DS when referring to pragmatics. Table 2 presents the list of production activities, labeled in terms of production mode, and the prosodic and linguistic features that can be trained. In order not to lose the playful component of the tool and its degree of playability, a small set of representative sentences of each category has been chosen, due to the limited number of activities that can be included in the video game. The table reports the different competences that are trained in each of the production activities of the learning game (comprehension and visual cognition complement the list of competences). The table is especially useful for speech therapists to select the DS down syndrome speaker, TD typically developing adults, OS individuals with another type of intellectual disability, TC typically developing children activity according to the needs of the student. In the following subsections, we explain the meaning of the different aspects depicted in the columns.

Prosodic function
Prosodic function includes intonation, prominence and the occurrence of prosodic boundaries (phrasing). In the modality column, the different intonational patterns related to the grammatical structure are considered: D stands for declarative sentences, Q for questions (including wh-questions and yes-no questions), E for exclamations (including commands, offers and invitations). The column Boundaries refers to the phrasing, that is, the organization of fluent speech into groups. Although there is no agreed account of the prosodic domains in Spanish, we distinguish two main ones: the intonation phrase and the intermediate phrase, which can be differentiated by their degree of prosodic autonomy. The arguments supporting the distinction come from perception and tonal inventory. On the one hand, speakers easily discriminate two levels of prosodic separation, normally associated with the finality or non-finality meaning of the sentence (Estebas-Vilaplana et al., 2015); on the other hand, the intermediate phrase is tonally marked with an accent, but the inventory of boundary tones that can appear in this position is more restricted than that which marks the end of an intonation phrase (Aguilar et al., 2021).
Prominence in speech can be conceived from several dimensions: phonetics, phonology, semantics, pragmatics, etc. (Cole, 2015). In this study, we adopt the perspective according to which prominence expresses the meaning of a statement in relation to the discursive context; in particular, how it serves to point out relevant information in statements, highlight an element or correct some information. The Prominence column classifies the target sentences of the activities according to word (W) or sentence (S) prominence. A word is prominent when it has been pronounced with prosodic salient features that put it in a noticeable position in the sentence. The whole sentence exhibits prominence (S) in those cases with a rhetorical and grandiloquent speech production within the video game context.
Although each target sentence is representative of one of the categories in the table, each of them also serves to observe the player's phonetic and phonological performance with respect to other phenomena, such as the position of the lexical accent and its phonetic realization, or the duration of syllables and pauses. R (Right) means move to the next activity with right as result; P (Poor) means move to the next activity but the oral activity could be better and the video game advances to the next activity; W (Wrong) means that the game offers a new attempt in which the player has to repeat the activity. The number in brackets represents the number of evaluations divided by the total number of recordings of people with DS, in percentages. #Rt is the number of raters PRAUTOCAL Corpus:... 205

Functions of language
To accommodate the learning objectives of the video game to Halliday's Theory of Language Development (Halliday, 1970), of general use in the speech and language therapy domain, we have classified the target sentences according to those functions that help the speaker to satisfy physical, emotional and social needs. Halliday refers to them instrumental, regulatory, interactional, personal, heuristic, imaginative and representational functions. Instrumental (N in Table 2 column Function) is when the speaker uses language to express her/his needs; regulatory (O) when language is used to tell others what to do; interactional (S) for making contact with others and form relationships; personal (P) when language is used to express feelings, opinions, and individual identity; heuristic (H) is used to seek information and ask questions; imaginative (I) to express creativity of poetic language; and representational (R) to give information and reporting facts. We especially focus on interactional and personal functions, in particular, verbal politeness and the prosodic expression of emotions. Verbal politeness can be considered from the perspectives of linguistic features, participants' socio-cultural background and their membership within a speech community, or it can be viewed from the ways to which it is applied in interpersonal utterances (Leech 2016). We focus on the politeness conveyed conveyed by words. The video game includes a set of target sentences that support politeness, including speech acts (basic units of linguistic communication with which an action is performed), such as greetings (G in Table 2), farewells (F), thank you (T) and courtesy (C) formulas (Haverkate, 1988;Vidal, 1995;Van Olmen, 2017). Concerning emotions, the video game presents activities related to surprise (O in Table 2 column Emotion), happiness (H), disgust (D), sadness (S). Fear and anger complete the list of basic emotions (Saarni et al., 2007). They have not been included because they did not fit with the story line of the video game.

Production mode
The prosodic categories and linguistic functions are addressed by different procedures, depending on whether the player is required to read (R in Table 2 column ProdMode), imitate a previous model (I) or produce an elicitated sentence on his/her own (E). Spontaneous speech (S) is only recorded at the end of the game, when the player has solved the full adventure and is asked about his/her feelings. The differences in the tasks enrich the information provided by the use of the video game concerning the prosodic and pragmatic skills of people with DS, as the relations between oral language skills and reading skills is a main topic of research in the literature of reading difficulties (Brooks, 2013;Roch et al., 2012Roch et al., , 2015. Table 3 and Fig. 2 describe the characteristics of PRAUTOCAL informants. The corpus is balanced in terms of gender (49 males, 41 females) and profile (50 individuals with intellectual disability and 40 TD speakers). The age range of both types of speaker is also similar, going from 13 to 42 years in the case of speakers with DS and from 6 to 68 years in the case of TD speakers; mean ages are 21.55 and 29.72, respectively. The oral productions of a group of TD children are included in PRAUTOCAL in order to have a control group that can match the mental age of DS speakers.

Informant profiles
Some of the participants with DS were given tests to account for individual variability by getting measurements of different developmental variables. Specifically, 5 participants of the C2 campaign and almost all of the participants (17) of the C3 campaign were evaluated using the following tests (campaigns are detailed in the following subsection). The Peabody Picture Vocabulary Scale-III (Dunn et al., 2006) was used to assess verbal mental age, the forward digit-span subtest included in the Wechsler Intelligence Scale for Children-IV (Corral et al., 2005) was used to evaluate verbal short-term memory and Raven's Coloured Progressive Matrices (Raven et al., 1995) served as a means to measure non-verbal cognitive level. The descriptive characteristics and scores obtained are shown in Fig. 2. The full PEPS-C battery in its Spanish version (Martínez-Castilla & Peppé, 2008) was also administered to participants in order to have specific measurements of prosody level. The mean percentage of success in the perception (MPercT) and in the production PEPS-C tasks (MProd) is also presented in Fig. 2.
The variety of intellectual profiles of the speakers with DS is high as far as verbal age (from 4.17 to 9.33 years) and non-verbal cognitive level (from 10 to 27 raw score) are concerned, as is usual in the case of DS (Chapman & Hesketh, 2001). Regarding the 5 speakers evaluated in campaign C2, the short-term verbal memory varies from 6.17 to 11.17 (mental age in years). As for prosodic competence, measured with the PEPS-C test, the corpus has individuals with diverse capabilities ranging from 50 to 84.4 (percentage of success) in the case of perception of prosody and from 31.3 to 82.8 in the case of production.
The Pearson correlation between these indices is low, the highest one being the correlation between MPrecT and MProdT (0.77). The rest of the indices presented in Fig. 2 correlate with each other, with a Pearson correlation factor below 0.51. The

Recording campaigns
PRAUTOCAL was gathered in six recording campaigns. The details of each campaign are shown in Table 4. Some users participated in multiple recording campaigns, and others only in one of them. The campaigns correspond to the evaluations of different versions of the video game which could imply different evaluation methodologies. Some of the campaigns are justified with the need of having data of TD speakers. The particularities of each campaigns are detailed in the following paragraphs. Campaign C1, the first recording campaign, was carried out using the initial version of the video game. With the aim of detecting possible deficiencies in the user interface and to determine how the users interacted with the video game, a usability test was carried out in the first game session (Corrales-Astorgano et al., 2016;González-Ferreras et al., 2017). The rest of the sessions were only dedicated to playing the game.
During the four game sessions, the role of the trainer (a teacher or speech therapist), who sat next to the player, was twofold: on the one hand, he/she evaluated the player's recordings and on the other, he/she helped players if necessary. The recordings gathered in this campaign were evaluated afterwards by a prosody expert, following the criteria described in Sect. 4.1.2.
A total of 51 users participated in the game sessions: 21 TD adults (12 males and 9 females), 13 people with DS (9 males and 4 females), 7 people with an intellectual disability of unknown origin (3 males and 4 females) and 10 TD children (6 males and 4 females). The first version of the video game included 7 production activities aimed at obtaining the sentences in Table 2.
Ten adults with DS (6 males and 4 females) participated in campaign C2. For sample selection, teachers working at the centers were asked to choose individuals with DS of different developmental levels. Participants played with the latest version of the video game (Aguilar, 2019).
As happened in the first campaign, the participants were supported by a speech and language therapist who was an expert at working with individuals with DS. The therapist explained the game, helped participants when needed and took notes about how each session developed. In addition, the therapist also assessed participants' speech production and thus monitored their rhythm of progress within the video game. The criteria followed by the therapist to evaluate the recordings are described in Sect. 4.1.2. Not all the evaluations were overseen by a speech therapist, so these evaluations were not taken into account when building the evaluation data.
Nineteen adults with DS participated in campaign C3. The session was also carried out using the PRADIA video game but, in this case, the therapist applied a rubric (see Sect. 4.1.3) to rate the quality of the audio recording, using a specifically designed mobile application.
Campaign C4 was carried out with the partipation of 11 people: 9 with DS (5 males and 4 females) and two females with an intellectual disability of unknown origin, but who were classmates of the DS students and shared their same learning activities. The PRADIA video game was used to obtain the recordings but, in this session, the evaluation of the recordings was made by an automatic module integrated in the video game, sending the recordings to be evaluated by a software application hosted in an external computer. The 11 users played the video game in the same room and were assisted by the therapist if help was needed. The automatic evaluation module is described in Sect. 5.
The same speakers included in the C4 campaign participated in campaign C5. In this session, the speakers played the video game alone. During the game session, the speakers were observed by the research team and the therapist. The therapist only assisted them if they got stuck or any technical issue happened. The same automatic evaluation module of the previous campaign was used to evaluate the recordings.
Campaign C6 was done with the aim of balancing the corpus with recordings of people without any intellectual disabilities. Fifteen TD adults (8 males and 7 females) and one TD child (female) recorded all the sentences included in the video game, but using a simplified version that only showed the production activities, removing all the game elements that were not necessary to record the sentences. These changes allowed important time savings for the speakers in the recording session.

Manual annotations 4.1 Human judgments of quality
The final aim of our project was to incorporate an intelligent control module which allows autonomous gaming. To introduce a module that substitutes the trainer on deciding whether the user must repeat the production activities, we managed to train a supervised classifier from human based evaluations of audios collected along the different testing sessions of the tools. In the first versions of the video game, affecting campaign C2, the evaluation of the audio utterances followed a three-level criterion described in Sect. 4.1.1. The limitations of these judgments for training the automatic component led us to carry out the off-line binary evaluation presented in Sect. 4.1.2. This second evaluation, affecting campaigns C1 and C2, allowed us to develop an automatic classifier to decide on the quality of the utterances. Seeking to improve the output of the classifier by providing information about the specific problems of the utterances, a new template-based evaluation was finally designed and applied, as described in Sect. 4.1.3. Also, an automatic binary classifier developed as described in Sect. 5, was applied to the samples of every campaign. Tables 5 and 6 show the different types of human and automatic-based evaluations of the recordings of the people with DS. These evaluations include 2151 automatic evaluations (all the recordings of the 42 DS speakers), 966 evaluations by a prosody expert (23 DS speakers from campaigns C1 and C2), 605 3-level based evaluations by a therapist (5 DS speakers from campaign C2) and 739

Therapist 3-level evaluations
As we have already stated in Corrales-Astorgano et al. (2019), in the first version of the video game, the sessions must be carried out with the support of an external assistant who guides the player with disability in the development of the activities, if he/she needs it, and who decides if the responses are adequate enough. The evaluation was done with an external keyboard using three different options: R right (Continue with right result), P poor (Continue, but the oral activity could be better), W wrong (the game offers a new attempt in which the player must repeat the activity).
The criteria was based on the categories of intonational phonology (that is, intonation, accent and prosodic organization) (Ladd, 2008). In the production of the sentences, the following were expected: an adjustment to the target modality (declarative, interrogative, exclamatory), a difference between lexical stress (stressed vs unstressed syllables) / accent (accented vs unaccented syllables), and a plausible division in prosodic groups with an appropriate allocation of pauses.
In his/her final decision, the therapist also took into consideration the motivational and emotional status of each participant in each session. Participants with DS needed different reinforcements according to their abilities and, therefore, the therapist can make the evaluation criteria more flexible. For example, if the participant was getting bored, anxious, or frustrated, the therapist could use the category P to allow the speaker to continue with the video game to reduce any negative valence of the therapy context.

Prosody expert binary evaluation
To complement the therapist evaluations, an expert in prosody evaluated the recordings of 23 speakers with DS in an offline mode (C1 and C2 campaigns), following the criteria detailed in section 4.1.1. In this evaluation, the environmental conditions implied in the use of the video game by DS speakers (lack of interest or level of frustration) were not considered, and the judgments were reduced to a binary decision (Right or Wrong production). The evaluation relied on perceptive criteria, without any acoustic analysis of the sentences, and the possible deficits in the segmental component (pronunciation of sounds) were not considered. Audios were shown in sequence and the expert could listen to each utterance as many times as necessary before providing the judgment. Even in the case of speakers with serious problems of intelligibility, the main criterion was whether the intonation had been produced in a way close enough to the expected one. More details can be found in Corrales-Astorgano et al. (2019).

Therapist template-based evaluation
In this case, during the playing sessions, the therapist was supplied with a specific purpose mobile application, not only for deciding whether the speaker continued or not, but also for rating the quality of the audio according to a set of criteria. The figures in Table 5 corresponding to R and W are derived from the criteria Continue (Y/N).
The following criteria were established: (2) The user produces a small part of the melodic curve correctly; (3) The user produces almost all of the melodic curve correctly; (4) The user produces all of the melodic curve correctly. Continue (Y/N): this is used by the therapist to let the user continue with the following game activity or to repeat the present one. The therapist could avoid using the value N depending on the state of the player: frustration, fatigue, etc.

Transcriptions
Every utterance of the DS recordings includes the literal transcription of the contents and a reference to the correct target production. The transcription format includes annotations of the disfluencies. We followed the criteria established in Shriberg (1994) to annotate disfluencies, which consider both interruption points and editing terms. Editing terms are used by speakers to correct the message and are a fundamental part of false starts and repetitions. Both interruption points caused by a disfluency and correctly placed pauses are marked with the symbol '',''. Thus the symbol '','' should be interpreted as an internal pause in the utterance. Researchers can distinguish between disfluent interruption points and pauses introduced to separate intonation groups using the target reference sentence.
The editing terms of the disfluencies have been marked in between the symbols < and >. The criteria for marking the boundaries of the editing terms is that, when removed, the resulting text should be as close as possible to the target sentence. In that sense, the target sentence would play the role of the underlying fluent sentence used in Adell et al. (2012) for modeling disfluencies.
The transcriptions have been included to represent the phonetic content of the utterance. Thus, the transcriptions include many out of vocabulary words, and some of them are unintelligible. The corpus includes marks of fillers, breathing and noises that are marked with the symbol #.
To illustrate the annotation results, Table 7 shows the transcription of the activity D1900. The introduction of interruption points in diverse places is the main reason for the presence of different versions. Repetitions and false starts are very frequent: 15 out of the 22 transcriptions include editing terms (in between < and >). Fillers appear 7 times (marked with the symbol #). Sect. 6.4 presents an analysis of mistakes and divergences with respect to the target sentence to show the impact of the production modality on fluency.

Automatic annotation using machine learning
In Corrales-Astorgano et al. (2019), we presented a cross validation process to build an automatic labeling system of the quality of the utterances. The openSmile toolkit (Eyben et al., 2013) was used to extract acoustic features from each recording and the GeMAPS feature set (Eyben et al., 2016) was selected, due to the variety of acoustic and prosodic features contained in this set, which includes frequency related, energy related, spectral and temporal features. The arithmetic mean and the standard deviation normalized by the arithmetic mean were calculated on these features. Furthermore, four additional temporal features were added: the silence and sounding percentages, silences per second and the mean silences. The silence and sounding intervals were calculated using the default values of the Praat software (Boersma, 2021), excluding the initial and final silence intervals from this parameterization procedure. The complete description of these features can be found in previous research (Corrales-Astorgano et al., 2018). We also used feature selection before training the classifier: the features were selected by measuring the  information gain of the training set and discarding the ones in which the information gain equalled zero.
Since the final aim of the automatic module of the video game is to decide whether the user can continue the game or should repeat the activity, we trained a binary classifier with Right (R) or Wrong (W) outputs. The Weka machine learning toolkit (Hall et al., 2009) was used to compare the performance of different types of classifiers: the C4.5 decision tree (DT), the multilayer perceptron (MLP) and the support vector machine (SVM). The stratified 10-fold cross-validation technique was used to create the training and testing datasets, as presented in Corrales-Astorgano et al. (2019). Classifiers was trained and tested with different versions of human labels, concluding that the best classification results are obtained with the SVM classifier (input: 21 preselected features including activity id; output: the human judgments assigned by the prosody expert described in Sect. 4.1.2). Having selected the input features and the target output, a new SVM classifier was retrained using all the prosody expert labels and included in the current version of the video game. The automatic labels generated with this classifier have been included in the corpus, resulting in 69% as R and 31% as W, as reported in Table 5.  Table 9 are coded with colors: G1 in black, G2 in red, G3 in green, G4 in blue PRAUTOCAL Corpus:... 215

Discussion and potential uses of the corpus
The results presented in Table 3 and Fig. 2 show that the heterogeneity of the target population is reflected in the corpus, with important ranges of variation in the different characteristic values and moderate correlation between them. As already shown by Cleland et al. (2010), the language development level of people with DS cannot be explained only in terms of their cognitive level. For example, obtaining better results in the non-verbal reasoning test does not necessarily imply better results in prosodic production tests: in our corpus, correlation between non verbal cognitive level (NVCL) and prosodic production competence (MProdT) is close to zero. Furthermore, language deficits have projections in different aspects (one of which is prosodic production). Even informants with relatively good verbal skills can obtain low results in prosody; for example 061 obtains the best results in the MPercT test (84.4), but a moderate result in the VA test (6.1 in a range that goes from 4.17 to 9.3).
Our video game is a useful resource for corpus collection purposes, not only because the user's motivation is kept, but also because oral activities are controlled, so the collected audios can be classified a priori in terms of how useful they could be for the study the way DS speakers use different prosodic and language functions. The recording process permits 2151 sentences to be collected in controlled conditions, avoiding informant fatigue. Unlike the high workload reported in previous works, therapists reported enjoyment in the users. The fact that the production activities are framed in a gamified story that is meaningful for students allows them to maintain a high degree of attention.
In the following subsections we highlight three aspects in which the corpus can shed light on concerns that are still open in the field: the contrast between speech in DS and TD individuals, the categorization of the speaker with DS in terms of his/her speech proficiency, and the impact of the production mode in the quality of the oral productions. Our goal here is to show how the corpus could be useful for these concerns. We do not go into any of these topics in any detail as they could be a subject of analysis in future research studies. In the preliminary analysis that we present, we stress the value of the extra information included in the corpus that can be useful regarding the aforementioned concerns: prosodic/acoustic features of the utterances to show a clear difference between TD speakers and speakers with DS (Sect. 6.1); manual annotation of the quality of the utterances to show possible types of speech proficiency in individuals with DS (Sect. 6.2); there exists a consistent relation between automatic labels and subjective scores (Sect. 6.3) and the transcriptions of the utterances to show the impact of the production mode in the quality of speech (Sect. 6.4).

Comparing typically developing and Down syndrome speakers
In Corrales-Astorgano et al. (2018), we showed the importance of analyzing audio recordings of DS speakers by using the acoustic signal to complement studies based on perception. We highlighted the relative importance of the different types of prosodic features on the characterization of this type of speakers. The present corpus is twice the size of the corpus used in the previous study; we repeat here part of the experiments to show the consistency of the results. Table 8 shows the classification results in the task of identifying the group of the speaker (TD or DS) of each utterance. In the TD group, only the samples of the adult speakers were selected. The acoustic features of the GeMAPS feature set were used (see Sect. 5) to train two automatic classifiers: the Linear Discriminant Analysis (LDA) and the Support Vector Machine (SVM). The scikit-learn library of the python language (Pedregosa et al., 2011) was used to train these classifiers and the Recursive Feature Elimination (RFE) process was used to automatically select those features in the data that contribute most to the prediction of the speaker group. For each feature type, the precision, recall and F1-score were obtained using the classification results of each classifier. We applied the Leave-One-Subject-Out cross validation (LOSO-cv) (Sakar et al., 2013) technique to calculate these results. This technique consists of training the classifier using the data of all the speakers except one and testing this classifier with the data of the speaker that was left out of the training data. This process was applied for all the speakers. The results were similar in both classifiers, but LDA works better with the Frequency (0.72 F1-score) feature set, while SVM is better with the Energy (0.79 F1-score) and All (0.93 F1-score) feature sets. The results are the same in Temporal (0.75 F1-score), Spectral (0.79 F1-score) and Frequency?Energy?Temporal (0.86 F1-score) feature sets.
In addition, when all features are jointly considered, the best classification results are obtained, regardless of the classifier. When input features are used in isolation, frequency related features lead to the worst classification performance. Using frequency, energy and temporal features together gives a noticeably better performance than using any of these groups separately. Spectral features lead to a worse performance than using either the frequency?energy?temporal group or the complete set of features altogether. These results are similar to those obtained in previous works (Corrales-Astorgano et al., 2018). These results show that the acoustic features can be used to discriminate between TD and DS speakers, since the distributions of their respective acoustic features are separable. PRAUTOCAL can be useful to analyze the specific characteristics of the speech of people with DS in greater depth, with the aim of improving the individual diagnosis of possible speech and language disorders that can be trained by speech therapy.

Heterogeneity of speakers with Down syndrome
Variability in the different linguistic skills of individuals with DS has often been documented (Fidler & Nadel, 2007). In Corrales-Astorgano et al. (2019), we showed how this variability affects the automatic prediction of the quality of the speakers' productions for those of campaign C2. In campaign C4, we introduced an evaluation template to deepen the analysis of the heterogeneity by using the prosodic dimensions presented in Sect. 4.1.3. Here we show how these dimensions allow users to be clustered into different categories according to their speaking proficiency. Table 9 presents the number of times (in percentage) the users who participated in campaign 4 obtained the highest score during the training session. Scores were introduced by the therapist following the procedure described in Sect. 4.1.2.
We applied k-means to cluster the speakers into classes. The best compromise between number of classes and distance to the centroids was obtained for k=4. Table 9 shows the centroids and Fig. 3 presents the inter-participant distances by using the two most discriminant dimensions: speech rate and melodic curve.
The G3 group represents speakers with the highest performance in all the dimensions; at the other extreme, the G4 group of speakers obtains the worst results in all the dimensions; G2 speakers have an inter-medium level with problems concerning speech rate; G1 speakers are similar to the G4 speakers but they seem to be less fluent.
The distribution of participants into classes reflects the high variety of production problems that characterize speakers with DS. These preliminary results show how useful human based annotation can be to identify the specific problems of the users. Further research into this is necessary to study how this classification can improve the effectiveness of the automatic systems that could be trained to predict the quality of oral productions.

Comparing automatic and manual annotations
The automatic annotations split the corpus into two classes according to the binary R/W label assigned. Concerning the relation between automatic and manual annotations, higher human scores are in the R group. The Chi-square test of independence was used to test whether the automatic annotations are independent of the manual ones, resulting in a p-value < 0.05, except in the case of the label. We hypothesize that the combination of different human scores can shed light on the reasons for the binary category W to be assigned, but this should be analyzed further in future research.
All the samples of the corpus have automatic labels generated with the same automatic classifier that is incorporated in the current version of the video game. This fact implies the limitation that part of the samples have been used to train the automatic system. We took this decision to guarantee that all the samples have a common reference and repeatable results, but it must be taken into account by future users of the corpus. We assume that the corpus is not a closed resource and additional automatic labels will be incorporated in future versions.
Building an alternative expert system that does not limit its judgments to binary R/W decisions, but complements these judgments with information about the fluency, intonation and speed of the utterances is also left for future work. This is a challenging task because the dependence both on the prosodic and language functions and on the production mode must be taken into account. We expect that the availability of this new open language resource, in combination with other sources of information such as unsupervised labels to be included in future work, will shed light on this promising research line.

Impact of the production mode on disfluencies
The relations between oral language skills and reading skills is a major topic of research in the literature of reading difficulties (Brooks, 2013), mainly due to their implications for school intervention practices in reading training. More specifically, in individuals with DS, the study of the relations between their oral language and their reading skills is a main field of research (Roch et al., 2015). It seems that their reading comprehension is better than their oral comprehension, and their oral language skills are worse than expected on the basis of their reading comprehension skills; these differences could be due to the different allocation of verbal memory resources (Roch et al., 2012). In that sense, we believe that the data obtained in this study through reading and non reading activities would be a valuable resource to analyze the possible differences and contribute to a better understanding of the relations between these two language processes in DS. Table 10 summarizes the disfluencies found in the recording transcriptions of the DS group in campaigns C1 to C5, showing a clear impact of the production mode on the type and quantity of the disfluencies transcribed. The production mode depends directly on the kind of activities and not on the campaign or groups of users. In descriptive terms, elicited speech shows the lowest number of interruption points (0.85 vs more than 1.44). Spontaneous speech and imitation introduce more editing terms and fillers than elicited and read speech (for example for ET 0.52 and 0.49 vs 0.38 and 0.30). Utterances produced in read mode are more similar to the expected ones than the rest of the utterances, with a lower number of insertions, deletions and substitutions (#D and #S are 0 in spontaneous speech as there is no reference).

Corpus description and distribution
The corpus content is organized in folders with the audio files, transcriptions, evaluations (both human and automatic), acoustic features and silent pauses of each of the utterances. The name of the files includes anonymized information of the speaker (id, gender, type and location) and information for identifying the activity, campaign and repetition. Additionally, the corpus distribution includes information about the activities, campaigns and intellectual profile of the speakers (the one reported in this paper). Finally, the log files of the game interaction are also included, as they include information that could be related with the reasoning or memory capabilities of the speakers or with the time of preparation before the oral productions.
Data collection design and trials were approved by the Ethics Committee of the University of Valladolid 1 . According to the rules of the committee, interested researchers should accept specific dissemination and use restrictions identical to those established in the PRAUTOCAL project (TIN2017-88858-C2-1-R) for accessing the audio files. The rest of the corpus is available for research purposes free of charge under license CC BY-NC, accessible from the web page of the research group 2 .

Conclusions
In this paper, we have presented a procedure for collecting an oral corpus based on the use of a video game with specific activities for training oral communication competences. The use of the video game has shown itself to be an efficient tool for collecting a corpus of the speech of individuals with DS, overcoming the difficulties in achieving this aim linked to the attention and memory problems of this type of informants.
The compiled corpus is a valuable resource for studying speech in DS that seems to surpass other corpora presented in the state of the art, not only in terms of the quantity of collected data, but also in terms of the quality, as it includes linguistic and functional information related to the specific oral turns and context; the evaluation judgments of the turns considering different dimensions; the transcriptions with annotations of the disfluencies; and the references of the speech of TD users for all the utterances.
We have shown how the recordings of speakers with DS have idiosyncratic characteristics when compared to those of TD speakers, when the high heterogeneity of the speech of DS speakers is represented in the corpus.
We expect the richness and variety of the information collected in the corpus will allow us to retrieve more knowledge about the specific characteristics of the speech of DS speakers. This could be used in future studies to enrich speech and language disorder diagnosis and improve specialized training activities.