{'fraction_non_alphanumeric': 0.029766693483507644, 'fraction_numerical': 0.007240547063555913, 'mean_word_length': 4.732718894009216, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Robust Query-by-example Spoken Term Detection for Unknown Words Using Speech Retrieval-oriented E2E ASR Modeling

Query-by-example spoken term detection (STD) systems can make good use of automatic speech recognition (ASR), especially when the error rate is low. However, ASR suffers from the out-of-vocabulary (OOV) problem. The OOV problem in the ASR stage has a significant impact on the performance of STD for speech retrieval and can generate false retrieval for query words. In recent studies, End-to-end (E2E) ASR systems have achieved competitive performance compared to traditional DNN-HMM ASR systems. It has also been shown that E2E ASR system can reduce the impact of the OOV problem by using characters or sub-words as the output unit during recognition. In this paper, we propose an improved method using E2E ASR modeling adapted to a speech retrieval task, based on the STD method that considers acoustic similarity at the sub-phone level. Experimental results using the NTCIR-12 SpokenQuery&Doc-2 task show that the STD method using E2E ASR improves retrieval performance over the STD method using DNN-HMM ASR. This is attributed the fact that E2E ASR was able to reduce the OOV problem for spoken documents and spoken queries.