261 citations · 588 across the 13 of their papers we have counts for
8 papers · 1 filter
Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities
Andros Tjandra, Nayan Singhal, David Zhang +4
End-to-end multilingual ASR has become more appealing because of several reasons such as simplifying the training and deployment process and positive performance transfer from high…
Biased Self-supervised learning for ASR
Florian L. Kreyssig, Yangyang Shi, Jinxi Guo +3
Self-supervised learning via masked prediction pre-training (MPPT) has shown impressive performance on a range of speech-processing tasks. This paper proposes a method to bias self…
Unified Speech-Text Pre-training for Speech Translation and Recognition
Yun Tang, Hongyu Gong, Ning Dong +8
We describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method incorporates four sel…
Generative Spoken Dialogue Language Modeling
Tu Anh Nguyen, Eugene Kharitonov, Jade Copet +8
We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled wi…
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai +3
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no l…
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Mike Lewis, Yinhan Liu, Naman Goyal +5
We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a…
Training ASR models by Generation of Contextual Information
Kritika Singh, Dmytro Okhonko, Jun Liu +8
Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales,…
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
Yongqiang Wang, Abdelrahman Mohamed, Duc Le +10
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional emb…