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20192025
most citedA Comparative Study on Transformer vs RNN in Speech Applications

788 citations · 885 across the 22 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024

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…

cs.CL2024

SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition

Yihan Wu, Soumi Maiti, Yifan Peng +6

Recent advancements in language models have significantly enhanced performance in multiple speech-related tasks. Existing speech language models typically utilize task-dependent pr…

cs.CL2023

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…

cs.CL2023

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…

cs.CL2019★ 788 cited

A Comparative Study on Transformer vs RNN in Speech Applications

Shigeki Karita, Nanxin Chen, Tomoki Hayashi +10

Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS…