Code-Switching Text Generation and Injection in Mandarin-English ASR

no code implementations • 20 Mar 2023 • Haibin Yu, Yuxuan Hu, Yao Qian, Ma Jin, Linquan Liu, Shujie Liu, Yu Shi, Yanmin Qian, Edward Lin, Michael Zeng

Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model

no code implementations • 6 Mar 2023 • Ruiqing Xue, Yanqing Liu, Lei He, Xu Tan, Linquan Liu, Edward Lin, Sheng Zhao

Neural text-to-speech (TTS) generally consists of cascaded architecture with separately optimized acoustic model and vocoder, or end-to-end architecture with continuous mel-spectrograms or self-extracted speech frames as the intermediate representations to bridge acoustic model and vocoder, which suffers from two limitations: 1) the continuous acoustic frames are hard to predict with phoneme only, and acoustic information like duration or pitch is also needed to solve the one-to-many problem, which is not easy to scale on large scale and noise datasets; 2) to achieve diverse speech output based on continuous speech features, complex VAE or flow-based models are usually required.

Language Modelling Large Language Model +1

Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training

no code implementations • 1 Mar 2023 • Eric Sun, Jinyu Li, Yuxuan Hu, Yimeng Zhu, Long Zhou, Jian Xue, Peidong Wang, Linquan Liu, Shujie Liu, Edward Lin, Yifan Gong

We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users during inference.

Language Identification

SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition

1 code implementation • 2 Dec 2022 • Yichong Leng, Xu Tan, Wenjie Liu, Kaitao Song, Rui Wang, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu

In this paper, we propose SoftCorrect with a soft error detection mechanism to avoid the limitations of both explicit and implicit error detection.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction

1 code implementation • 23 Nov 2022 • Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin

Since the error rate of the incorrect sentence is usually low (e. g., 10\%), the correction model can only learn to correct on limited error tokens but trivially copy on most tokens (correct tokens), which harms the effective training of error correction.

Sentence speech-recognition +1

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition

1 code implementation • Findings (EMNLP) 2021 • Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Tao Qin, Xiang-Yang Li, Edward Lin, Tie-Yan Liu

Although multiple candidates are generated by an ASR system through beam search, current error correction approaches can only correct one sentence at a time, failing to leverage the voting effect from multiple candidates to better detect and correct error tokens.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

This Text Has the Scent of Starbucks: A Laplacian Structured Sparsity Model for Computational Branding Analytics

no code implementations • EMNLP 2013 • William Yang Wang, Edward Lin, John Kominek

Facial Expression Recognition (FER) Text Classification