Title: Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
Authors: Ronan Collobert, Christian Puhrsch, Gabriel Synnaeve
Keywords: Deep learning, Speech, Structured prediction
TL;DR: We propose convnet models and new sequence criterions for training end-to-end letter-based speech systems.
Abstract:
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 (Graves et al., 2006) while being simpler. We show competitive results in word error rate on the Librispeech corpus (Panayotov et al., 2015) with MFCC features, and promising results from raw waveform.
Venue: Submitted to ICLR 2017