5 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…
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…
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…
SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Sirui Li, Shuai Wang, Zhijun Liu +3
Speech pre-processing techniques such as denoising, de-reverberation, and separation, are commonly employed as front-ends for various downstream speech processing tasks. However, t…
Multi-Level Speaker Representation for Target Speaker Extraction
Ke Zhang, Junjie Li, Shuai Wang +4
Target speaker extraction (TSE) relies on a reference cue of the target to extract the target speech from a speech mixture. While a speaker embedding is commonly used as the refere…