We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We model the output vocabulary of about 100,000 words directly using deep bi-directional LSTM RNNs with CTC loss. The model is trained on 125,000 hours of semi-supervised acoustic training data, which enables us to alleviate the data sparsity problem for word models. We show that the CTC word models work very well as an end-to-end all-neural speech recognition model without the use of traditional context-dependent sub-word phone units that require a pronunciation lexicon, and without any language model removing the need to decode. We demonstrate that the CTC word models perform better than a strong, more complex, state-of-the-art baseline with sub-word units.
@article{arxiv.1610.09975,
title = {Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition},
author = {Hagen Soltau and Hank Liao and Hasim Sak},
journal= {arXiv preprint arXiv:1610.09975},
year = {2016}
}