Don't speak too fast: The impact of data bias on self-supervised speech
models

Don't speak too fast: The impact of data bias on self-supervised speech models

15 October 2021

Papers citing "Don't speak too fast: The impact of data bias on self-supervised speech models"

11 / 11 papers shown

Title
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget Andy T. Liu Yi-Cheng Lin Haibin Wu Stefan Winkler Hung-yi Lee 56 2 0 09 Sep 2024
Improving Fairness in Speaker Recognition Gianni Fenu Giacomo Medda Mirko Marras Giacomo Meloni 31 19 0 29 Apr 2021
Similarity Analysis of Self-Supervised Speech Representations Yu-An Chung Yonatan Belinkov James R. Glass SSL 46 37 0 22 Oct 2020
TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech Andy T. Liu Shang-Wen Li Hung-yi Lee SSL 100 356 0 12 Jul 2020
Vector-Quantized Autoregressive Predictive Coding Yu-An Chung Hao Tang James R. Glass SSL 35 115 0 17 May 2020
Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition Shaoshi Ling Yuzong Liu Julian Salazar Katrin Kirchhoff SSL 43 139 0 03 Dec 2019
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders Andy T. Liu Shu-Wen Yang Po-Han Chi Po-Chun Hsu Hung-yi Lee SSL 95 372 0 25 Oct 2019
Generative Pre-Training for Speech with Autoregressive Predictive Coding Yu-An Chung James R. Glass SSL 48 174 0 23 Oct 2019
TabNet: Attentive Interpretable Tabular Learning Sercan O. Arik Tomas Pfister LMTD 112 1,314 0 20 Aug 2019
Speech Model Pre-training for End-to-End Spoken Language Understanding Loren Lugosch Mirco Ravanelli Patrick Ignoto Vikrant Singh Tomar Yoshua Bengio SyDa AuLLM 60 349 0 07 Apr 2019
Deep contextualized word representations Matthew E. Peters Mark Neumann Mohit Iyyer Matt Gardner Christopher Clark Kenton Lee Luke Zettlemoyer NAI 104 11,520 0 15 Feb 2018