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

2016-09-11 · arXiv 2016 9 · Ronan Collobert, Christian Puhrsch, Gabriel Synnaeve

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 alignment of phonemes. We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC while being simpler. We show competitive results in word error rate on the Librispeech corpus with MFCC features, and promising results from raw waveform.

📄 PDF Abstract BibTeX arXiv:1609.03193

Code (9)

CorrelAid/codingchallenge1020_team1 tf
JuliusKunze/speechless tf
MrMao/wav2letter torch
ashwin9999/Capstone-Speech-to-SQL tf
ashwin9999/speech-recognition-CNN tf
eric-erki/wav2letter torch
mailong25/vietnamese-speech-recognition pytorch
msalhab96/SpeeQ pytorch
silversparro/wav2letter.pytorch pytorch

Tasks

Speech Recognition

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