2 citations · 5 across the 9 of their papers we have counts for
9 papers
WST: Weakly Supervised Transducer for Automatic Speech Recognition
Dongji Gao, Chenda Liao, Changliang Liu +5
The Recurrent Neural Network-Transducer (RNN-T) is widely adopted in end-to-end (E2E) automatic speech recognition (ASR) tasks but depends heavily on large-scale, high-quality anno…
Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition
Dongji Gao, Hainan Xu, Desh Raj +3
Training automatic speech recognition (ASR) systems requires large amounts of well-curated paired data. However, human annotators usually perform "non-verbatim" transcription, whic…
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Han Zhu, Dongji Gao, Gaofeng Cheng +3
When labeled data is insufficient, semi-supervised learning with the pseudo-labeling technique can significantly improve the performance of automatic speech recognition. However, p…
HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation
Cihan Xiao, Henry Li Xinyuan, Jinyi Yang +4
We introduce HK-LegiCoST, a new three-way parallel corpus of Cantonese-English translations, containing 600+ hours of Cantonese audio, its standard traditional Chinese transcript,…
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts
Dongji Gao, Matthew Wiesner, Hainan Xu +3
This paper presents a novel algorithm for building an automatic speech recognition (ASR) model with imperfect training data. Imperfectly transcribed speech is a prevalent issue in…
PHONEix: Acoustic Feature Processing Strategy for Enhanced Singing Pronunciation with Phoneme Distribution Predictor
Yuning Wu, Jiatong Shi, Tao Qian +2
Singing voice synthesis (SVS), as a specific task for generating the vocal singing voice from a music score, has drawn much attention in recent years. SVS faces the challenge that…
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR
Jiatong Shi, Chan-Jan Hsu, Holam Chung +5
Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised mode…
EURO: ESPnet Unsupervised ASR Open-source Toolkit
Dongji Gao, Jiatong Shi, Shun-Po Chuang +4
This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR). EURO adopts the…
Decoupling recognition and transcription in Mandarin ASR
Jiahong Yuan, Xingyu Cai, Dongji Gao +3
Much of the recent literature on automatic speech recognition (ASR) is taking an end-to-end approach. Unlike English where the writing system is closely related to sound, Chinese c…