Bi-Cheng Yan


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DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
Yi-Cheng Wang | Hsin-Wei Wang | Bi-Cheng Yan | Chi-Han Lin | Berlin Chen
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently been proposed, which normally build on pho-netic-level edit distance algorithms and have shown impressive NEC performance. However, as the named entity (NE) list grows, the problems of phonetic confusion in the NE list are exacerbated; for example, homophone ambiguities increase substantially. In view of this, we proposed a novel Description Augmented Named entity CorrEctoR (dubbed DANCER), which leverages entity descriptions to provide additional information to facilitate mitigation of phonetic con-fusion for NEC on ASR transcription. To this end, an efficient entity description augmented masked language model (EDA-MLM) comprised of a dense retrieval model is introduced, enabling MLM to adapt swiftly to domain-specific entities for the NEC task. A series of experiments conducted on the AISHELL-1 and Homophone datasets confirm the effectiveness of our modeling approach. DANCER outperforms a strong baseline, the phonetic edit-distance-based NEC model (PED-NEC), by a character error rate (CER) reduction of about 7% relatively on AISHELL-1 for named entities. More notably, when tested on Homophone that contain named entities of high phonetic confusion, DANCER offers a more pronounced CER reduction of 46% relatively over PED-NEC for named entities. The code is available at


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Exploring the Integration of E2E ASR and Pronunciation Modeling for English Mispronunciation Detection
Hsin-Wei Wang | Bi-Cheng Yan | Yung-Chang Hsu | Berlin Chen
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

There has been increasing demand to develop effective computer-assisted language training (CAPT) systems, which can provide feedback on mispronunciations and facilitate second-language (L2) learners to improve their speaking proficiency through repeated practice. Due to the shortage of non-native speech for training the automatic speech recognition (ASR) module of a CAPT system, the corresponding mispronunciation detection performance is often affected by imperfect ASR. Recognizing this importance, we in this paper put forward a two-stage mispronunciation detection method. In the first stage, the speech uttered by an L2 learner is processed by an end-to-end ASR module to produce N-best phone sequence hypotheses. In the second stage, these hypotheses are fed into a pronunciation model which seeks to faithfully predict the phone sequence hypothesis that is most likely pronounced by the learner, so as to improve the performance of mispronunciation detection. Empirical experiments conducted a English benchmark dataset seem to confirm the utility of our method.


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使用字典學習法於強健性語音辨識(The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]
Bi-Cheng Yan | Chin-Hong Shih | Shih-Hung Liu | Berlin Chen
Proceedings of the 28th Conference on Computational Linguistics and Speech Processing (ROCLING 2016)

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使用字典學習法於強健性語音辨識 (The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]
Bi-Cheng Yan | Chin-Hong Shih | Shih-Hung Liu | Berlin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016