6 citations · 7 across the 10 of their papers we have counts for
10 papers
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Yifan Peng, Jinchuan Tian, Brian Yan +13
Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech dat…
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…
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
William Chen, Jiatong Shi, Brian Yan +6
Multilingual self-supervised learning (SSL) has often lagged behind state-of-the-art (SOTA) methods due to the expenses and complexity required to handle many languages. This furth…
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark
Jiatong Shi, Dan Berrebbi, William Chen +8
Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks…
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
Brian Yan, Jiatong Shi, Yun Tang +13
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offlin…
Avoid Overthinking in Self-Supervised Models for Speech Recognition
Dan Berrebbi, Brian Yan, Shinji Watanabe
Self-supervised learning (SSL) models reshaped our approach to speech, language and vision. However their huge size and the opaque relations between their layers and tasks result i…
More Speaking or More Speakers?
Dan Berrebbi, Ronan Collobert, Navdeep Jaitly +1
Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In spite of these advances, to the best o…
Continuous Pseudo-Labeling from the Start
Dan Berrebbi, Ronan Collobert, Samy Bengio +2
Self-training (ST), or pseudo-labeling has sparked significant interest in the automatic speech recognition (ASR) community recently because of its success in harnessing unlabeled…
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation
Dan Berrebbi, Jiatong Shi, Brian Yan +3
Self-Supervised Learning (SSL) models have been successfully applied in various deep learning-based speech tasks, particularly those with a limited amount of data. However, the qua…
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Brian Yan, Chunlei Zhang, Meng Yu +6
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type. In this work, we propose a genera…