2 citations · 3 across the 11 of their papers we have counts for
11 papers
TokAN: Accent Normalization Using Self-Supervised Speech Tokens
Qibing Bai, Shuai Wang, Yuhan Du +3
Accent normalization (AN) seeks to convert non-native (L2) accented speech into standard (L1) speech while preserving speaker identity. The current techniques either require natura…
Bridging What the Model Thinks and How It Speaks: Expressive Speech Generation via Self-Aware Intent-Realization Alignment
Kuang Wang, Lai Wei, Ping Lin +8
Speech Language Models (SLMs) exhibit strong semantic understanding, yet often fail to translate this capacity into expressive acoustic realization, producing speech with flattened…
Controllable Accent Normalization via Discrete Diffusion
Qibing Bai, Yuhan Du, Tom Ko +3
Existing accent normalization methods do not typically offer control over accent strength, yet many applications-such as language learning and dubbing-require tunable accent retent…
CosyAccent: Duration-Controllable Accent Normalization Using Source-Synthesis Training Data
Qibing Bai, Shuhao Shi, Shuai Wang +3
Accent normalization (AN) systems often struggle with unnatural outputs and undesired content distortion, stemming from both suboptimal training data and rigid duration modeling. I…
Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data
Qibing Bai, Sho Inoue, Shuai Wang +3
Accent normalization converts foreign-accented speech into native-like speech while preserving speaker identity. We propose a novel pipeline using self-supervised discrete tokens a…
LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
Xi Chen, Songyang Zhang, Qibing Bai +2
We introduces LLaST, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech transl…
Autoregressive Diffusion Transformer for Text-to-Speech Synthesis
Zhijun Liu, Shuai Wang, Sho Inoue +2
Audio language models have recently emerged as a promising approach for various audio generation tasks, relying on audio tokenizers to encode waveforms into sequences of discrete s…
Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
Shuai Wang, Qibing Bai, Qi Liu +5
Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets. To boost the system performance, researchers leverage lar…
A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis
Qibing Bai, Tom Ko, Yu Zhang
In human speech, the attitude of a speaker cannot be fully expressed only by the textual content. It has to come along with the intonation. Declarative questions are commonly used…
Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation
Qianqian Dong, Fengpeng Yue, Tom Ko +3
Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently. The task is very challenging due to data scarcity and complex speech-to-speech mapping. In th…
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
Rui Wang, Qibing Bai, Junyi Ao +6
Self-supervised speech representation learning has shown promising results in various speech processing tasks. However, the pre-trained models, e.g., HuBERT, are storage-intensive…