Ronan Collobert

Analysis of CNN-based speech recognition system using raw speech as input

2015

Automatic speech recognition systems typically model the rela-tionship between the acoustic speech signal and the phones in two separate steps: feature extraction and classifier training. In our recent works, we have shown that, in the …

Wav2Letter: an End-to-End ConvNet-based Speech Recognition System

2016 · arXiv (Cornell University)

This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters, with transcribed speech, without the need for force …

Fully Convolutional Speech Recognition

2018 · arXiv (Cornell University)

Current state-of-the-art speech recognition systems build on recurrent neural networks for acoustic and/or language modeling, and rely on feature extraction pipelines to extract mel-filterbanks or cepstral coefficients. In this paper we present an alternative approach …

Libri-Light: A Benchmark for ASR with Limited or No Supervision

2020

We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books from the LibriVox project. It contains over 60K …