2 citations · 2 across the 18 of their papers we have counts for
18 papers
Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens
Daigo Takizawa, Tomohiko Nakamura, Samuele Cornell +3
Neural audio codecs (NACs) have become popular for obtaining speech representations as discrete tokens. Beyond compression, discrete tokens can be used to train self-supervised lea…
ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era
Masao Someki, Alexander Polok, Carlos Carvalho +14
Recent speech research involves increasingly large datasets, complex models, and diverse experimental workflows. However, existing frameworks require substantial engineering effort…
Bagpiper-Edit: Zero-Shot Open-Ended Audio Editing via Rich-Caption
Xun Gong, Jinchuan Tian, Haoran Wang +3
Current text-guided audio editing methods rely on paired training data, predefined operation templates, and separate processing pipelines across speech, music, and sound. We presen…
Evaluating Large Language Models Abilities for Addressee, Turn-change, and Next Speaker Prediction in Meetings
Ryo Fukuda, Takatomo Kano, Siddhant Arora +7
We investigate turn-taking in multimodal multi-party conversations using large language models (LLMs). We construct an evaluation framework for three tasks: addressee detection, tu…
An Empirical Recipe for Universal Phone Recognition
Shikhar Bharadwaj, Chin-Jou Li, Kwanghee Choi +4
Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive. Highly performant English-focused models d…
Robust Training of Singing Voice Synthesis Using Prior and Posterior Uncertainty
Yiwen Zhao, Jiatong Shi, Yuxun Tang +2
Singing voice synthesis (SVS) has seen remarkable advancements in recent years. However, compared to speech and general audio data, publicly available singing datasets remain limit…
OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
William Chen, Jinchuan Tian, Yifan Peng +3
Neural scaling laws offer valuable insights for designing robust sequence processing architectures. While these laws have been extensively characterized in other modalities, their…
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…
ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration
Masao Someki, Kwanghee Choi, Siddhant Arora +7
We introduce ESPnet-EZ, an extension of the open-source speech processing toolkit ESPnet, aimed at quick and easy development of speech models. ESPnet-EZ focuses on two major aspec…
ESPnet-Codec: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speech
Jiatong Shi, Jinchuan Tian, Yihan Wu +17
Neural codecs have become crucial to recent speech and audio generation research. In addition to signal compression capabilities, discrete codecs have also been found to enhance do…
Towards Robust Speech Representation Learning for Thousands of Languages
William Chen, Wangyou Zhang, Yifan Peng +7
Self-supervised learning (SSL) has helped extend speech technologies to more languages by reducing the need for labeled data. However, models are still far from supporting the worl…
On the Evaluation of Speech Foundation Models for Spoken Language Understanding
Siddhant Arora, Ankita Pasad, Chung-Ming Chien +9
The Spoken Language Understanding Evaluation (SLUE) suite of benchmark tasks was recently introduced to address the need for open resources and benchmarking of complex spoken langu…
On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models
Jinchuan Tian, Yifan Peng, William Chen +3
The Open Whisper-style Speech Model (OWSM) series was introduced to achieve full transparency in building advanced speech-to-text (S2T) foundation models. To this end, OWSM models…
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets
Jiatong Shi, Shih-Heng Wang, William Chen +8
ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of language identification and automatic speech recognition (ASR). This benchmark treats the models as featur…
YODAS: Youtube-Oriented Dataset for Audio and Speech
Xinjian Li, Shinnosuke Takamichi, Takaaki Saeki +3
In this study, we introduce YODAS (YouTube-Oriented Dataset for Audio and Speech), a large-scale, multilingual dataset comprising currently over 500k hours of speech data in more t…
AugSumm: towards generalizable speech summarization using synthetic labels from large language model
Jee-weon Jung, Roshan Sharma, William Chen +2
Abstractive speech summarization (SSUM) aims to generate human-like summaries from speech. Given variations in information captured and phrasing, recordings can be summarized in mu…
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond
Jiatong Shi, William Chen, Dan Berrebbi +10
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual s…
EFFUSE: Efficient Self-Supervised Feature Fusion for E2E ASR in Low Resource and Multilingual Scenarios
Tejes Srivastava, Jiatong Shi, William Chen +1
Self-Supervised Learning (SSL) models have demonstrated exceptional performance in various speech tasks, particularly in low-resource and multilingual domains. Recent works show th…