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A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality

This paper introduces a comparison of deep learning-based techniques for the MOS prediction task of synthesised speech in the Interspeech VoiceMOS challenge. Using the data from the main track of the VoiceMOS challenge we explore both existing predictors and propose new ones. We evaluate two groups of models: NISQA-based models and techniques based on fine-tuning the self-supervised learning (SSL) model wav2vec2_base. Our findings show that a simplified version of NISQA with 40% fewer parameters achieves results close to the original NISQA architecture on both utterance-level and system-level performances. Pre-training NISQA with the NISQA corpus improves utterance-level performance but shows no benefit on the system-level performance. Also, the NISQA-based models perform close to LDNet and MOSANet, 2 out of 3 baselines of the challenge. Fine-tuning wav2vec2_base shows superior performance than the NISQA-based models. We explore the mismatch between natural and synthetic speech and discovered that the performance of the SSL model drops consistently when fine-tuned on natural speech samples. We show that adding CNN features with the SSL model does not improve the baseline performance. Finally, we show that the system type has an impact on the predictions of the non-SSL models.


Introduction and Related Work
Text-to-speech (TTS) and voice conversion (VC) are two fundamental speech synthesis tasks [1,2]. The perceptual quality of speech synthesis affects the acceptability of a system and needs to be evaluated. Subjective listening tests are the most reliable way to assess speech synthesis quality but they are time-consuming and not usable in real-time applications. Objective quality metrics have been proposed to predict the mean opinion score (MOS) without human intervention [3]. A state-of-art evaluation of speech synthesis systems published in 2018 [4] calls for some new best practices in speech synthesis research. Among the best practices, the authors analysed some issues with objective metrics for TTS. For instance, some objective metrics developed for natural speech have been adapted to evaluate speech synthesis but they tend to focus on spectral features and consider prosody as a secondary problem. Evaluating speech synthesis quality with objective metrics suffers from generalization when evaluated on unseen speech synthesis systems or unseen listening tests used to collect the ground truth. To tackle the above-mentioned challenges, deep learning-based MOS predictors emerged and the VoiceMOS challenge has been proposed. The VoiceMOS challenge goal is to predict MOS of synthesised speech encouraging data-driven approaches.
In this section, we briefly describe some of the existing deep learning-based MOS prediction techniques. AutoMOS [5] learns a mapping between MOS and raw waveform. It is based on a family of models based on Long Short-Term Memory (LSTM) networks. AutoMOS has been trained and evaluated on one speaker only. However, the speaker relevantly affects the quality of TTS systems [6] which calls to evaluate AutoMOS in a multi-speaker scenario. MOSNet [7] is a model based on combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) designed for VC system quality. MOSNet performs well on system-level and fairly on utterance-level [7]. A similar trend has been found on the VoiceMOS challenge test set [3] when fine-tuning the pre-trained MOSNet on the VoiceMOS challenge training set with data augmentation. It must be noted that MOSNet trained from scratch and the pre-trained MOSNet without fine-tuning perform poorly on the challenge dataset [3]. In addition, it has been found that MOSNet suffers when evaluating the quality of real-world speech recordings [8]. LDNet [9] is a network that uses individual listener ratings to predict MOS. The speech signal and the listener ID are both used as input of one individual network. LDNet represents one of the 3 baselines used for the VoiceMOS challenge. Another baseline proposed for the challenge is fine-tuning the self-supervised learning (SSL) model wav2vec2 large and wav2vec2 base [10]. Fine-tuning SSL models showed promising performance in audio tasks [11] and other speech tasks [12], even surpassing supervised learning approaches.
Motivated by the availability of the task and the datasets provided by the challenge, we contribute as follows. First, we study how MOS predictors proposed for natural speech perform on synthetic speech and we propose a model based on CNNs and temporal max pooling. Next, we explore if the fine-tuned wav2vec2 base model could be improved and analyse the mismatch between natural speech and synthetic speech. Finally, we evaluate the performance by the system type.

CNN with Temporal Max Pooling
In this section we describe a model based on CNNs that we propose for the VoiceMOS challenge. Learning features from log Mel spectrograms with 2D CNNs has been proven to be promising for speech quality prediction [13,14]. Our model consists of CNN with temporal max pooling (CMP) which is based on the same framewise CNN of NISQA [13]. NISQA is a MOS predictor developed for speech quality prediction which was not originally evaluated on speech synthesis. The NISQA convolutional network maps a spectrogram patch of size 48×15 (48 Mel bands, 15 time frames) to 6 × 1 × 64 features where 64 is the number of filters of the last convolutional layer. Unlike NISQA which uses self-attention to model the time dependency and temporal attention pooling, we simplify the network. We connect a global pooling layer (GAP) to get 1 × 1 × 64 for each spectrogram patch and we use temporal max pooling to squeeze the time dimension obtaining 64 features for each speech sample. After max pooling, we connect an output layer that predicts MOS. Using temporal max pooling gives us a model 40% smaller than NISQA in terms of the number of parameters (≈135K vs. ≈218K).

Pre-Trained CNN with Temporal Max Pooling
Given the availability of large unlabelled speech data, we pre-train the convolutional part of CMP with a convolutional autoencoder (CMP-AE) trained on a different unlabelled dataset. Deep autoencoder features have been already used for non-intrusive speech [15,14] and audio-video quality prediction [16]. The encoder uses the same CNN as NISQA. After the CNN we connect a global pooling layer (GAP) as in CMP to get 1 × 1 × 64. This is followed by a linear layer that compresses each spectrogram patch to a representation of size 1 × 10. This last linear layer is discarded when fine-tuning to predict MOS. We noticed that GAP combined with a linear layer showed a more robust representation than attaching directly a linear layer after the convolutional network. In this way, the network has fewer parameters, which reduces training and inference time. The decoder is composed of a linear layer (from 1 × 10 to 1 × 384), reshaping to a tensor of size 6 × 1 × 64, and 3 transposed convolutional layers that reconstruct each spectrogram patch.

Self-Supervised Learning (SSL) Models
The models wav2vec2 large and wav2vec2 base fine-tuned on the VoiceMOS training set are the best challenge baselines [3].
In our experiments, we use wav2vec2 base that maps each waveform time frame to 768 features. We explore if adding different features to wav2vec2 base would further improve MOS prediction performance.
The method is illustrated in Fig 1. The network combines the following features: features extracted from the fine-tuned wav2vec2 base model on the VoiceMOS training set (1 × 768), the predicted MOS of the fine-tuned wav2vec2 base model (1× 1), and features from the temporal max pooling layer of CMP-AE. The output layer maps all the features (1 × (768 + 1 + 64)) to a single value which is the predicted MOS. This method described above is called CMP-AE+wM+wF where wM indicates the wav2vec2 base MOS and wF refers to the wav2vec2 base features. We also evaluate a combination of MOS and CMP-AE only (CMP-AE+wM) During training, we learn the weights of the output layer and we fine-tune the CNN of CMP-AE. We do not further fine-tune the wav2vec2 base model with its output layer.

Training
In our experiments we use 3 datasets. The VoiceMOS dataset provided by the challenge, the NISQA corpus 1 , and the PSTN 1 https://github.com/gabrielmittag/NISQA corpus [17]. From the VoiceMOS challenge we only use the main track which does not include out-of-domain data. The VoiceMOS dataset is composed of 187 speech synthesis systems including TTS, VC, and natural speech. The speech synthesis systems include the ones from the Blizzard challenge (BC), from the voice conversion challenge (VCC), and ESPNet systems 2 . Speech is synthesised with several utterances and in a multi-speaker setting. A listening test has been conducted collecting 8 rates for each speech sample [18]. The NISQA corpus has been designed to develop and evaluate NISQA. It is composed of 14K natural speech samples degraded with both realworld conditions (e.g. video call services and mobile phones) and synthetic degradations (e.g. codecs, background noise). From the NISQA corpus we only used the training set as described in the next section. The PSTN corpus includes ≈80K speech samples with public switched telephone network degradations obtained with real phone calls.
The SSL-based models are trained on the VoiceMOS training set using the Adam optimizer with a learning rate 0.001 and batch size 4. We minimize the L1 loss function. To control overfitting we use early stopping with mean squared error (MSE) and set patience equal to 20 epochs. We also fine-tune wav2vec2 base on the training set of the NISQA corpus and a subset of PSTN corpus with the same hyper-parameters used for the VoiceMOS training set.
CMP-AE and CMP are trained on the VoiceMOS training set with the same hyperparameters and same early stopping except for the batch size which is increased to 32. The autoencoder is trained on the LbriSpeech100hr dataset [19] using Adam optimizer with a learning rate 0.001 and batch size 32. We stopped training after 200 epochs.

Results
Performance evaluation is conducted with the same metrics of the challenge and consists of MSE, Spearman's rank correlation coefficient (SRCC), linear correlation coefficient (LCC) and Kendall's tau coefficient (KTAU). Each metric is calculated on utterance-level i.e. ground truth MOS against predicted MOS on each speech sample and on system-level where every utterance is aggregated by synthesis system. The following proposed models are evaluated:    • CMP is the model using the NISQA framewise CNN model with temporal max pooling. The model weights are randomly initialized.
• CMP-AE is CMP where the CNN is pre-trained with the autoencoder.
• CMP-AE+wM combines CMP-AE with the predicted MOS of wav2vec2 base.
• CMP-AE+wM+wF is the model illustrated in Fig. 1 which combines CMP-AE with predicted MOS of wav2vec2 base and wav2vec2 base features.
It must be noted that CMP and CMP-AE use a shifted and scaled sigmoid in the output layer to make sure that the predicted MOS is within the range 1-5. We noticed that on these 2 models the sigmoid improved both utterance-level and systemlevel performance on the validation set. The usage of the sigmoid turned out to be not useful for all the other models which are trained without it. The results on the validation and test set are shown in Table 1 and Table 2 respectively. The state-of-theart models include: • NISQA Scratch is NISQA best version as reported in [13] with self-attention and temporal attention pooling trained on the VoiceMOS challenge training set.
• NISQA Pre-Trained is the same model of NISQA Scratch but pre-trained on the NISQA Corpus.
• wav2vec2 base is the SSL wav2vec2 base model with an output layer fine-tuned on the VoiceMOS challenge training set.
• wav2vec2 base NC is the SSL wav2vec2 base model fine-tuned on the training sets of the NISQA corpus. We used this model to evaluate how wav2vec base fine-tuned on natural speech performs on synthetic speech.
• wav2vec2 base PSTN is the SSL wav2vec2 base model fine-tuned on a subset of the PSTN dataset. We extract 7K speech samples to avoid using a dataset too much larger than the VoiceMOS training set. We made sure to have a similar MOS distribution of the full size dataset. This model is also used to evaluate the mismatch between fine-tuning on natural speech and testing on synthetic speech.
• wav2vec2 large is the SSL wav2vec2 large model with an output layer fine-tuned on the VoiceMOS challenge training set. It is the best baseline for the challenge.
• MOSA-Net is the second baseline of the challenge originally proposed for speech quality prediction which uses a CNN-RNN architecture and both time and spectral features [20].
• LDNet is the third baseline of the challenge which we already described in Section 1.
The models CMP-AE+wM+wF and CMP-AE+wM show better results than the baseline wav2vec2 base on the test set on all the metrics. Fine-tuning wav2vec2 base with natural speech (PSTN and NISQA corpus) shows lower performance than finetuning with the VoiceMOS training set. This suggests that finetuning using a dataset that is out-of-domain is a weaker approach. Also, the models that are fine-tuned with natural speech show worse results than the non-SSL models.

Performance Analysis Based on the System Type
We evaluated the performance of some models based on the system type: BC, VCC, and ESPNet. The results including system MSE and system SRCC are shown in Fig. 2.
All the models perform better on the VCC data. The worst system MSE is obtained on the BC systems and the worst SRCC is on the ESPNet systems. Pre-training with the autoencoder (CMP-AE) has a positive impact with respect to the non pretrained version (CMP) on BCC and VC systems. For ESPNet data there is a performance drop when evaluated with SRCC. It must be noted that the performance drop on the ESPNet does not have a huge impact on the overall performance (Table 2) because the test set includes more BC and VCC data than ESPNet. So, overall CMP-AE performs better than CMP for system SRCC as discussed in the section above. Similarly NISQA pre-trained shows a performance drop compared to NISQA from scratch for system MSE on ESPNet but better performance on BCC and VC. This means that for the ESPNet systems the performance indicates a case of negative transfer for both LibriSpeech data (CMP-AE vs CMP) and the NISQA corpus (NISQA pre-trained vs NISQA scratch).
CMP shows higher SRCC than NISQA scratch on the ES-PNet systems and very close SRCC on BC and VCC data. This result is in contrast with the evaluation performance conducted on natural speech [13] where the self-attention layer with temporal attention pooling performs better than more simple approaches such as temporal max pooling or temporal average pooling.  Figure 2: Performance evaluation using system MSE (a) and system SRCC (b) based on the system type (BC, VCC, and ESPNet).

Discussion
The models NISQA Scratch, NISQA Pre-Trained, LDNet, CMP-AE perform similarly on the system-level performance.
Only MOSA-Net shows slightly better results. A key observation is that CMP-AE shows almost the same results as NISQA Scratch with a network that is 40% smaller. This implies that pre-training with autoencoder and using temporal max pooling could replace the self-attention layer and temporal attention pooling on this data. This might be useful in applications where memory issues occur. The results of fine-tuning the SSL models are aligned with findings from computer vision [21] and various speech tasks [22]. Even though fine-tuning the SSL models results in superior performance, we showed that the training set has a crucial role. We fine-tuned the wav2vec2 base model with natural speech and showed that the performance drops remarkably compared to all the other models.
The model CMP-AE+Wm+Wf is also composed of an extra layer compared to the wav2vec2 base baseline (Fig 1). This is necessary to map the combined features to a final predicted MOS score. To check if the better test set results reported in Table 2 are because of the CMP-AE features and not due to the extra layer we create a control model here called wM + wF. The control model is composed of a linear layer that maps the 769-dimensional feature space (wav2vec2 base features concatenated to the predicted MOS of the fine-tuned wav2vec2 base) to the final predicted MOS. So we discard the CNN features. In Table 3 we compare the control model against the average of 5 runs of CMP-AE+wM+WF and show that the CNN features poorly contribute to improving the wav2vec2 base baseline. A possible reason could be that learning features with an SSL task from raw waveform shows greater flexibility than using hand-crafted features such as log Mel spectrogram with supervised learning as shown in other audio tasks [22,11]. So the CNN features do not capture new useful information. However we found that the extra layer with a MOS predicted in a previous training adds benefit. Further investigation is required to understand why adding an extra layer that takes the predicted MOS as input improves the performance.
Finally, we showed that the MOS predictors evaluated in this paper do not show the same performance on all the system types ( Figure 1). ESPNet systems consistently show worse performance in all the MOS predictors while the best results are obtained on the VCC systems. It has been shown that ESPNet are the models exhibiting the highest subjective MOS while VCC systems show the lowest [18]. This suggests a possible cause of the performance drop could be related to the intrinsic properties of high-quality synthesised speech or due to some biases in the subjective MOS of high-quality speech.

Conclusions
In this paper, we compared deep learning-based MOS predictors for speech synthesis and evaluated modifications to certain networks. Using the VoiceMOS challenge dataset we found that fine-tuned SSL models perform remarkably better than other MOS predictors developed for natural speech but suffer when fine-tuned on natural speech. We showed that NISQA can be simplified using temporal max pooling instead of self-attention and temporal attention pooling. We showed that the system type has an impact on the performance of the non-SSL models with the best results obtained for VCC samples and worse performance obtained on BC and ESPNet samples.
In the future, we will extend the evaluation on the out-of-domain track and we will explore an analysis of network sizes since SSL networks are usually large. Finally, we will explore the cause concerning the influence of the system type on the performance.

Acknowledgements
This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 17/RC-PhD/3483 and 17/RC/2289 P2. The work of EB was supported by a Turing Fellowship.