16 citations · 27 across the 21 of their papers we have counts for
22 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…
PhiNet: Speaker Verification with Phonetic Interpretability
Yi Ma, Shuai Wang, Tianchi Liu +1
Despite remarkable progress, automatic speaker verification (ASV) systems typically lack the transparency required for high-accountability applications. Motivated by how human expe…
Borderless Long Speech Synthesis
Xingchen Song, Di Wu, Dinghao Zhou +12
Most existing text-to-speech (TTS) systems either synthesize speech sentence by sentence and stitch the results together, or drive synthesis from plain-text dialogues alone. Both a…
AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow
Duojia Li, Shuhan Zhang, Zihan Qian +5
In target speaker extraction (TSE), we aim to recover target speech from a multi-talker mixture using a short enrollment utterance as reference. Recent studies on diffusion and flo…
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…
G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition
Jing Peng, Ziyi Chen, Haoyu Li +7
We study timestamped speaker-attributed automatic speech recognition (SA-ASR) for long-form, multi-party speech with overlap. In this setting, chunk-wise inference must preserve me…
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…
Direct Preference Optimization for Speech Autoregressive Diffusion Models
Zhijun Liu, Dongya Jia, Xiaoqiang Wang +4
Autoregressive diffusion models (ARDMs) have recently been applied to speech generation, achieving state-of-the-art (SOTA) performance in zero-shot text-to-speech. By autoregressiv…
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…
Causal Self-supervised Pretrained Frontend with Predictive Code for Speech Separation
Wupeng Wang, Zexu Pan, Xinke Li +2
Speech separation (SS) seeks to disentangle a multi-talker speech mixture into single-talker speech streams. Although SS can be generally achieved using offline methods, such a pro…
Context-Aware Two-Step Training Scheme for Domain Invariant Speech Separation
Wupeng Wang, Zexu Pan, Jingru Lin +2
Speech separation seeks to isolate individual speech signals from a multi-talk speech mixture. Despite much progress, a system well-trained on synthetic data often experiences perf…
ExPO: Explainable Phonetic Trait-Oriented Network for Speaker Verification
Yi Ma, Shuai Wang, Tianchi Liu +1
In speaker verification, we use computational method to verify if an utterance matches the identity of an enrolled speaker. This task is similar to the manual task of forensic voic…
Hierarchical Control of Emotion Rendering in Speech Synthesis
Sho Inoue, Kun Zhou, Shuai Wang +1
Emotional text-to-speech synthesis (TTS) aims to generate realistic emotional speech from input text. However, quantitatively controlling multi-level emotion rendering remains chal…
Speech Separation with Pretrained Frontend to Minimize Domain Mismatch
Wupeng Wang, Zexu Pan, Xinke Li +2
Speech separation seeks to separate individual speech signals from a speech mixture. Typically, most separation models are trained on synthetic data due to the unavailability of ta…
M-Vec: Matryoshka Speaker Embeddings with Flexible Dimensions
Shuai Wang, Pengcheng Zhu, Haizhou Li
Fixed-dimensional speaker embeddings have become the dominant approach in speaker modeling, typically spanning hundreds to thousands of dimensions. These dimensions are hyperparame…
On the effectiveness of enrollment speech augmentation for Target Speaker Extraction
Junjie Li, Ke Zhang, Shuai Wang +3
Deep learning technologies have significantly advanced the performance of target speaker extraction (TSE) tasks. To enhance the generalization and robustness of these algorithms wh…
E1 TTS: Simple and Fast Non-Autoregressive TTS
Zhijun Liu, Shuai Wang, Pengcheng Zhu +2
This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distributi…
MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversion
Sho Inoue, Shuai Wang, Wanxing Wang +3
In accented voice conversion or accent conversion, we seek to convert the accent in speech from one another while preserving speaker identity and semantic content. In this study, w…
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…
Hierarchical Emotion Prediction and Control in Text-to-Speech Synthesis
Sho Inoue, Kun Zhou, Shuai Wang +1
It remains a challenge to effectively control the emotion rendering in text-to-speech (TTS) synthesis. Prior studies have primarily focused on learning a global prosodic representa…
Fine-Grained Quantitative Emotion Editing for Speech Generation
Sho Inoue, Kun Zhou, Shuai Wang +1
It remains a significant challenge how to quantitatively control the expressiveness of speech emotion in speech generation. In this work, we present a novel approach for manipulati…