Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
Table 1: Bidirectional-LSTM acoustic models trained on data sets of varying sizes.
['Model', 'Training Criterion', 'Size', 'Data (hrs)', 'WER(%)']
The acoustic model is a 3-state HMM with 6400 CD triphone states. By training with a sequence-level state-MBR criterion and using a two-pass adapted decoding setup, the best we could do was 24.0% with a 650 hour training set. Contrast this with adding more semi-supervised training data: at 5000 hours, we reduced the error rate to 21.2% for the same model size. This type of model improves with the amount of training data and there is little difference between CE and CTC training criteria.