14 citations · 17 across the 13 of their papers we have counts for
8 papers · 1 filter
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
Self-supervised Speech Representations Still Struggle with African American Vernacular English
Kalvin Chang, Yi-Hui Chou, Jiatong Shi +4
Underperformance of ASR systems for speakers of African American Vernacular English (AAVE) and other marginalized language varieties is a well-documented phenomenon, and one that r…
SingMOS: An extensive Open-Source Singing Voice Dataset for MOS Prediction
Yuxun Tang, Jiatong Shi, Yuning Wu +1
In speech generation tasks, human subjective ratings, usually referred to as the opinion score, are considered the "gold standard" for speech quality evaluation, with the mean opin…
SingOMD: Singing Oriented Multi-resolution Discrete Representation Construction from Speech Models
Yuxun Tang, Yuning Wu, Jiatong Shi +1
Discrete representation has shown advantages in speech generation tasks, wherein discrete tokens are derived by discretizing hidden features from self-supervised learning (SSL) pre…
TokSing: Singing Voice Synthesis based on Discrete Tokens
Yuning Wu, Chunlei zhang, Jiatong Shi +3
Recent advancements in speech synthesis witness significant benefits by leveraging discrete tokens extracted from self-supervised learning (SSL) models. Discrete tokens offer highe…
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection
Yongyi Zang, Jiatong Shi, You Zhang +8
Recent singing voice synthesis and conversion advancements necessitate robust singing voice deepfake detection (SVDD) models. Current SVDD datasets face challenges due to limited c…
The Interspeech 2024 Challenge on Speech Processing Using Discrete Units
Xuankai Chang, Jiatong Shi, Jinchuan Tian +7
Representing speech and audio signals in discrete units has become a compelling alternative to traditional high-dimensional feature vectors. Numerous studies have highlighted the e…
VISinger2+: End-to-End Singing Voice Synthesis Augmented by Self-Supervised Learning Representation
Yifeng Yu, Jiatong Shi, Yuning Wu +2
Singing Voice Synthesis (SVS) has witnessed significant advancements with the advent of deep learning techniques. However, a significant challenge in SVS is the scarcity of labeled…