6 citations · 8 across the 20 of their papers we have counts for
9 papers · 1 filter
Training Dynamics-Aware Multi-Factor Curriculum Learning for Target Speaker Extraction
Yun Liu, Xuechen Liu, Xiaoxiao Miao +1
Target speaker extraction (TSE) aims to isolate a specific speaker's voice from multi-speaker mixtures. Despite strong benchmark results, real-world performance often degrades due…
Explaining Speaker and Spoof Embeddings via Probing
Xuechen Liu, Junichi Yamagishi, Md Sahidullah +1
This study investigates the explainability of embedding representations, specifically those used in modern audio spoofing detection systems based on deep neural networks, known as…
Libri2Vox Dataset: Target Speaker Extraction with Diverse Speaker Conditions and Synthetic Data
Yun Liu, Xuechen Liu, Xiaoxiao Miao +1
Target speaker extraction (TSE) is essential in speech processing applications, particularly in scenarios with complex acoustic environments. Current TSE systems face challenges in…
Improving curriculum learning for target speaker extraction with synthetic speakers
Yun Liu, Xuechen Liu, Junichi Yamagishi
Target speaker extraction (TSE) aims to isolate individual speaker voices from complex speech environments. The effectiveness of TSE systems is often compromised when the speaker c…
The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction
Wen-Chin Huang, Szu-Wei Fu, Erica Cooper +5
We present the third edition of the VoiceMOS Challenge, a scientific initiative designed to advance research into automatic prediction of human speech ratings. There were three tra…
Disentangling the Prosody and Semantic Information with Pre-trained Model for In-Context Learning based Zero-Shot Voice Conversion
Zhengyang Chen, Shuai Wang, Mingyang Zhang +3
Voice conversion (VC) aims to modify the speaker's timbre while retaining speech content. Previous approaches have tokenized the outputs from self-supervised into semantic tokens,…
Generating Speakers by Prompting Listener Impressions for Pre-trained Multi-Speaker Text-to-Speech Systems
Zhengyang Chen, Xuechen Liu, Erica Cooper +2
This paper proposes a speech synthesis system that allows users to specify and control the acoustic characteristics of a speaker by means of prompts describing the speaker's traits…
Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction
Aditya Ravuri, Erica Cooper, Junichi Yamagishi
Predicting audio quality in voice synthesis and conversion systems is a critical yet challenging task, especially when traditional methods like Mean Opinion Scores (MOS) are cumber…
Speaker-Text Retrieval via Contrastive Learning
Xuechen Liu, Xin Wang, Erica Cooper +2
In this study, we introduce a novel cross-modal retrieval task involving speaker descriptions and their corresponding audio samples. Utilizing pre-trained speaker and text encoders…