1 citations · 2 across the 9 of their papers we have counts for
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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…
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking
Brian Yan, Vineel Pratap, Shinji Watanabe +1
Multilingual Automatic Speech Recognition (ASR) models are typically evaluated in a setting where the ground-truth language of the speech utterance is known, however, this is often…
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation
Motoi Omachi, Brian Yan, Siddharth Dalmia +2
The black-box nature of end-to-end speech translation (E2E ST) systems makes it difficult to understand how source language inputs are being mapped to the target language. To solve…
Towards Zero-Shot Code-Switched Speech Recognition
Brian Yan, Matthew Wiesner, Ondrej Klejch +2
In this work, we seek to build effective code-switched (CS) automatic speech recognition systems (ASR) under the zero-shot setting where no transcribed CS speech data is available…
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…
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models
Siddhant Arora, Siddharth Dalmia, Brian Yan +3
End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation. However, thes…