Search Results for author: Liqun Deng

Found 7 papers, 1 papers with code

CorrectSpeech: A Fully Automated System for Speech Correction and Accent Reduction

no code implementations12 Apr 2022 Daxin Tan, Liqun Deng, Nianzu Zheng, Yu Ting Yeung, Xin Jiang, Xiao Chen, Tan Lee

This study extends our previous work on text-based speech editing to developing a fully automated system for speech correction and accent reduction.

speech editing Speech Recognition

Reducing language context confusion for end-to-end code-switching automatic speech recognition

no code implementations28 Jan 2022 Shuai Zhang, Jiangyan Yi, Zhengkun Tian, JianHua Tao, Yu Ting Yeung, Liqun Deng

Training end-to-end (E2E) automatic speech recognition (ASR) systems for code-switching is known to be a challenging problem because of the lack of data compounded by the increased language context confusion due to the presence of more than one language.

Automatic Speech Recognition

CCA-MDD: A Coupled Cross-Attention based Framework for Streaming Mispronunciation detection and diagnosis

no code implementations16 Nov 2021 Nianzu Zheng, Liqun Deng, Wenyong Huang, Yu Ting Yeung, Baohua Xu, Yuanyuan Guo, Yasheng Wang, Xin Jiang, Qun Liu

The encoder of CCA-MDD consists of a conv-Transformer network based streaming acoustic encoder and an improved cross-attention named coupled cross-attention (CCA).

Multi-Task Learning

EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion

no code implementations4 Jul 2021 Daxin Tan, Liqun Deng, Yu Ting Yeung, Xin Jiang, Xiao Chen, Tan Lee

This paper presents the design, implementation and evaluation of a speech editing system, named EditSpeech, which allows a user to perform deletion, insertion and replacement of words in a given speech utterance, without causing audible degradation in speech quality and naturalness.

speech editing

Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization

no code implementations18 Jun 2021 Disong Wang, Liqun Deng, Yu Ting Yeung, Xiao Chen, Xunying Liu, Helen Meng

Such systems are particularly susceptible to domain mismatch where the training and testing data come from the source and target domains respectively, but the two domains may differ in terms of speech stimuli, disease etiology, etc.

Multi-Task Learning Unsupervised Domain Adaptation

VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion

1 code implementation18 Jun 2021 Disong Wang, Liqun Deng, Yu Ting Yeung, Xiao Chen, Xunying Liu, Helen Meng

One-shot voice conversion (VC), which performs conversion across arbitrary speakers with only a single target-speaker utterance for reference, can be effectively achieved by speech representation disentanglement.

Disentanglement Quantization +1

Unified Mandarin TTS Front-end Based on Distilled BERT Model

no code implementations31 Dec 2020 Yang Zhang, Liqun Deng, Yasheng Wang

The front-end module in a typical Mandarin text-to-speech system (TTS) is composed of a long pipeline of text processing components, which requires extensive efforts to build and is prone to large accumulative model size and cascade errors.

Knowledge Distillation Language Modelling +1

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