84 citations · 183 across the 32 of their papers we have counts for
12 papers · 1 filter
Improving Language Model-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts
Shun Lei, Yixuan Zhou, Liyang Chen +8
Zero-shot text-to-speech (TTS) synthesis aims to clone any unseen speaker's voice without adaptation parameters. By quantizing speech waveform into discrete acoustic tokens and mod…
QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning
Haohan Guo, Fenglong Xie, Jiawen Kang +3
This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS quality with lower supervised data requirements via Vector-Quantized Self-Supervised Speech Repres…
Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information
Jie Chen, Changhe Song, Deyi Tuo +4
For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays an important role in producing natural and intelligible speech. Although inter-utterance linguistic in…
MSStyleTTS: Multi-Scale Style Modeling with Hierarchical Context Information for Expressive Speech Synthesis
Shun Lei, Yixuan Zhou, Liyang Chen +4
Expressive speech synthesis is crucial for many human-computer interaction scenarios, such as audiobooks, podcasts, and voice assistants. Previous works focus on predicting the sty…
Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator
Lingwei Meng, Jiawen Kang, Mingyu Cui +3
Multi-talker overlapped speech poses a significant challenge for speech recognition and diarization. Recent research indicated that these two tasks are inter-dependent and compleme…
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One
Lingwei Meng, Jiawen Kang, Mingyu Cui +3
Although automatic speech recognition (ASR) can perform well in common non-overlapping environments, sustaining performance in multi-talker overlapping speech recognition remains c…
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations
Haohan Guo, Fenglong Xie, Xixin Wu +2
This paper aims to enhance low-resource TTS by reducing training data requirements using compact speech representations. A Multi-Stage Multi-Codebook (MSMC) VQ-GAN is trained to le…
A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS
Haohan Guo, Fenglong Xie, Frank K. Soong +2
We propose a Multi-Stage, Multi-Codebook (MSMC) approach to high-performance neural TTS synthesis. A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is us…
Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion
Haibin Wu, Jiawen Kang, Lingwei Meng +5
Recent years have witnessed the extraordinary development of automatic speaker verification (ASV). However, previous works show that state-of-the-art ASV models are seriously vulne…
Neural Architecture Search for Speech Emotion Recognition
Xixin Wu, Shoukang Hu, Zhiyong Wu +2
Deep neural networks have brought significant advancements to speech emotion recognition (SER). However, the architecture design in SER is mainly based on expert knowledge and empi…
Spoofing-Aware Speaker Verification by Multi-Level Fusion
Haibin Wu, Lingwei Meng, Jiawen Kang +5
Recently, many novel techniques have been introduced to deal with spoofing attacks, and achieve promising countermeasure (CM) performances. However, these works only take the stand…
A Multi-Scale Time-Frequency Spectrogram Discriminator for GAN-based Non-Autoregressive TTS
Haohan Guo, Hui Lu, Xixin Wu +1
The generative adversarial network (GAN) has shown its outstanding capability in improving Non-Autoregressive TTS (NAR-TTS) by adversarially training it with an extra model that di…