End-to-end multilingual ASR has become more appealing because of several reasons such as simplifying the training and deployment process and positive performance ...
Neural network pruning compresses automatic speech recognition (ASR) models effectively. However, in multilingual ASR, language ...
This paper presents Cocktail HuBERT, a self-supervised learning framework that generalizes to mixture speech using a masked pseudo source separation objective.
In this work, we propose LA-VocE, a new two-stage approach that predicts mel-spectrograms from noisy audiovisual speech via a transformer-based architecture, and then converts...
Visual speech recognition (VSR), also known as lip reading, is the task of recognizing speech content based on visual lip movements. VSR has a wide range of applications in real-world scenarios such as helping the hearing- impaired perceive human speech and improving automatic speech recognition (ASR) in noisy environments.
we build a new dataset comprising a moderate amount of labeled data for 1,107 languages and another dataset of unlabeled speech in 3,809 languages (§3). We leverage ....
We propose a learning objective that formalizes differences in perceptual quality, by using domain knowledge of acoustic-phonetics.
Through this paper, we introduce tools and a set of models to estimate such known metrics using deep neural networks.
This paper introduces an end-to-end echo cancellation network with a streaming cross-attention alignment (SCA). Our proposed method can handle unaligned inputs without requiring...
In this work, we analyze the effect of changing synthesized target speech for direct S2ST models.
