2 citations · 4 across the 12 of their papers we have counts for
12 papers
HiPPO: Exploring A Novel Hierarchical Pronunciation Assessment Approach for Spoken Languages
Bi-Cheng Yan, Hsin-Wei Wang, Fu-An Chao +3
Automatic pronunciation assessment (APA) seeks to quantify a second language (L2) learner's pronunciation proficiency in a target language by offering timely and fine-grained diagn…
Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection
Tzu-Ting Yang, Hsin-Wei Wang, Yi-Cheng Wang +1
Code-switching-where multilingual speakers alternately switch between languages during conversations-still poses significant challenges to end-to-end (E2E) automatic speech recogni…
An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition
Yi-Cheng Wang, Li-Ting Pai, Bi-Cheng Yan +3
End-to-end (E2E) automatic speech recognition (ASR) models have become standard practice for various commercial applications. However, in real-world scenarios, the long-tailed natu…
ConPCO: Preserving Phoneme Characteristics for Automatic Pronunciation Assessment Leveraging Contrastive Ordinal Regularization
Bi-Cheng Yan, Wei-Cheng Chao, Jiun-Ting Li +4
Automatic pronunciation assessment (APA) manages to evaluate the pronunciation proficiency of a second language (L2) learner in a target language. Existing efforts typically draw o…
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
Yi-Cheng Wang, Hsin-Wei Wang, Bi-Cheng Yan +2
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 f…
An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement
Tzu-Ting Yang, Hsin-Wei Wang, Yi-Cheng Wang +2
With the massive developments of end-to-end (E2E) neural networks, recent years have witnessed unprecedented breakthroughs in automatic speech recognition (ASR). However, the codes…
Leveraging Language ID to Calculate Intermediate CTC Loss for Enhanced Code-Switching Speech Recognition
Tzu-Ting Yang, Hsin-Wei Wang, Berlin Chen
In recent years, end-to-end speech recognition has emerged as a technology that integrates the acoustic, pronunciation dictionary, and language model components of the traditional…
Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss Function for Automatic Pronunciation Assessment
Bi-Cheng Yan, Hsin-Wei Wang, Yi-Cheng Wang +3
Automatic pronunciation assessment (APA) manages to quantify the pronunciation proficiency of a second language (L2) learner in a language. Prevailing approaches to APA normally le…
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning
Yi-Cheng Wang, Tzu-Ting Yang, Hsin-Wei Wang +2
Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input. Through voice, the voice search (VS) system can provide a more natur…
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition
Bi-Cheng Yan, Hsin-Wei Wang, Shih-Hsuan Chiu +2
Conversational speech normally is embodied with loose syntactic structures at the utterance level but simultaneously exhibits topical coherence relations across consecutive utteran…
Exploring Non-Autoregressive End-To-End Neural Modeling For English Mispronunciation Detection And Diagnosis
Hsin-Wei Wang, Bi-Cheng Yan, Hsuan-Sheng Chiu +2
End-to-end (E2E) neural modeling has emerged as one predominant school of thought to develop computer-assisted language training (CAPT) systems, showing competitive performance to…
Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech
Bi-Cheng Yan, Shao-Wei Fan Jiang, Fu-An Chao +1
End-to-end (E2E) neural models are increasingly attracting attention as a promising modeling approach for mispronunciation detection and diagnosis (MDD). Typically, these models ar…