Publications de Apoorv Vyas | EPFL Graph Search

Efficient Transformer-Based Speech Recognition

Training deep neural network based Automatic Speech Recognition (ASR) models often requires thousands of hours of transcribed data, limiting their use to only a few languages. Moreover, current state-of-the-art acoustic models are based on the Transformer ...

EPFL2022

Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model

Hervé Bourlard, Apoorv Vyas

In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the overfitting issues with connectionist temporal classification (CTC) training to reduce its performance gap with flat-start lattice-free MMI (E2E-LFMMI) for autom ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Lattice-Free Mmi Adaptation Of Self-Supervised Pretrained Acoustic Models

Hervé Bourlard, Apoorv Vyas

In this work, we propose lattice-free MMI (LFMMI) for supervised adaptation of self-supervised pretrained acoustic model. We pretrain a Transformer model on thousand hours of untranscribed Librispeech data followed by supervised adaptation with LFMMI on th ...

IEEE2021

Fast Transformers with Clustered Attention

François Fleuret, Angelos Katharopoulos, Apoorv Vyas

Transformers have been proven a successful model for a variety of tasks in sequence modeling. However, computing the attention matrix, which is their key component, has quadratic complexity with respect to the sequence length, thus making them prohibitivel ...

2020