9 papers
Contextual Biasing for Streaming ASR via CTC-based Word Spotting
Kai-Chen Tsai, Tien-Hong Lo, Yun-Ting Sun +1
Contextual biasing is essential to improving the recognition of rare and domain-specific words in an automatic speech recognition (ASR) system. While numerous methods have been pro…
MALEFA: Multi-grAnularity Learning and Effective False Alarm Suppression for Zero-shot Keyword Spotting
Lo-Ya Li, Tien-Hong Lo, Jeih-Weih Hung +2
User-defined keyword spotting (KWS) without resorting to domain-specific pre-labeled training data is of fundamental importance in building adaptable and personalized voice interfa…
SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization
Chien-Chun Wang, En-Lun Yu, Jeih-Weih Hung +2
Voice activity detection (VAD) is essential for speech-driven applications, but remains far from perfect in noisy and resource-limited environments. Existing methods often lack rob…
ConSep: a Noise- and Reverberation-Robust Speech Separation Framework by Magnitude Conditioning
Kuan-Hsun Ho, Jeih-weih Hung, Berlin Chen
Speech separation has recently made significant progress thanks to the fine-grained vision used in time-domain methods. However, several studies have shown that adopting Short-Time…
What do neural networks listen to? Exploring the crucial bands in Speech Enhancement using Sinc-convolution
Kuan-Hsun Ho, Jeih-weih Hung, Berlin Chen
This study introduces a reformed Sinc-convolution (Sincconv) framework tailored for the encoder component of deep networks for speech enhancement (SE). The reformed Sincconv, based…
Naaloss: Rethinking the objective of speech enhancement
Kuan-Hsun Ho, En-Lun Yu, Jeih-weih Hung +1
Reducing noise interference is crucial for automatic speech recognition (ASR) in a real-world scenario. However, most single-channel speech enhancement (SE) generates "processing a…
A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multi-granular Pronunciation Assessment
Fu-An Chao, Tien-Hong Lo, Tzu-I Wu +2
Automatic Pronunciation Assessment (APA) plays a vital role in Computer-assisted Pronunciation Training (CAPT) when evaluating a second language (L2) learner's speaking proficiency…
3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment
Fu-An Chao, Tien-Hong Lo, Tzu-I Wu +2
As an indispensable ingredient of computer-assisted pronunciation training (CAPT), automatic pronunciation assessment (APA) plays a pivotal role in aiding self-directed language le…
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…