no code implementations • ROCLING 2021 • Hsin-Wei Wang, Bi-Cheng Yan, Yung-Chang Hsu, Berlin Chen
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.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 26 Mar 2024 • Yi-Cheng Wang, Hsin-Wei Wang, Bi-Cheng Yan, Chi-Han Lin, Berlin Chen
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.
no code implementations • 3 Oct 2023 • Bi-Cheng Yan, Hsin-Wei Wang, Yi-Cheng Wang, Jiun-Ting Li, Chi-Han Lin, Berlin Chen
Automatic pronunciation assessment (APA) manages to quantify the pronunciation proficiency of a second language (L2) learner in a language.
no code implementations • 4 Sep 2023 • Yi-Cheng Wang, Tzu-Ting Yang, Hsin-Wei Wang, Bi-Cheng Yan, Berlin Chen
Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 5 Nov 2021 • Bi-Cheng Yan, Hsin-Wei Wang, Shih-Hsuan Chiu, Hsuan-Sheng Chiu, Berlin Chen
Conversational speech normally is embodied with loose syntactic structures at the utterance level but simultaneously exhibits topical coherence relations across consecutive utterances.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 1 Nov 2021 • Hsin-Wei Wang, Bi-Cheng Yan, Hsuan-Sheng Chiu, Yung-Chang Hsu, Berlin Chen
In addition, we design and develop a pronunciation modeling network stacked on top of the NAR E2E models of our method to further boost the effectiveness of MD&D.
no code implementations • 31 Aug 2021 • Bi-Cheng Yan, Shao-Wei Fan Jiang, Fu-An Chao, Berlin Chen
End-to-end (E2E) neural models are increasingly attracting attention as a promising modeling approach for mispronunciation detection and diagnosis (MDD).
1 code implementation • 4 Jul 2021 • Fu-An Chao, Shao-Wei Fan Jiang, Bi-Cheng Yan, Jeih-weih Hung, Berlin Chen
Due to the unprecedented breakthroughs brought about by deep learning, speech enhancement (SE) techniques have been developed rapidly and play an important role prior to acoustic modeling to mitigate noise effects on speech.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 4 Mar 2021 • Bi-Cheng Yan, Berlin Chen
Furthermore, our model can achieve comparable mispronunciation detection performance in relation to state-of-the-art E2E MDD models that take input the standard handcrafted acoustic features.
no code implementations • 25 May 2020 • Bi-Cheng Yan, Meng-Che Wu, Hsiao-Tsung Hung, Berlin Chen
Mispronunciation detection and diagnosis (MDD) is a core component of computer-assisted pronunciation training (CAPT).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2