5 citations · 14 across the 13 of their papers we have counts for
6 papers · 1 filter
Cross-domain Single-channel Speech Enhancement Model with Bi-projection Fusion Module for Noise-robust ASR
Fu-An Chao, Jeih-weih Hung, Berlin Chen
In recent decades, many studies have suggested that phase information is crucial for speech enhancement (SE), and time-domain single-channel speech enhancement techniques have show…
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…
Towards Robust Mispronunciation Detection and Diagnosis for L2 English Learners with Accent-Modulating Methods
Shao-Wei Fan Jiang, Bi-Cheng Yan, Tien-Hong Lo +2
With the acceleration of globalization, more and more people are willing or required to learn second languages (L2). One of the major remaining challenges facing current mispronunc…
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
Fu-An Chao, Shao-Wei Fan Jiang, Bi-Cheng Yan +2
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 m…
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition
Shih-Hsuan Chiu, Tien-Hong Lo, Fu-An Chao +1
How to effectively incorporate cross-utterance information cues into a neural language model (LM) has emerged as one of the intriguing issues for automatic speech recognition (ASR)…
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020
Fu-An Chao, Tien-Hong Lo, Shi-Yan Weng +3
This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-20…