Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition
Table 3: LSTM RNN CTC word acoustic models. The WERs and out of vocabulary (OOV) rates for word models are on heldout data with no decoding or language model. WERs in the last column are computed ignoring utterances containing OOVs.
['Vocabulary', 'OOV', 'WER (%)', 'In vocab. WER (%)']
We have experimented with CTC word models with various vocabulary sizes where the output layer directly predicts words rather than phonemes. We have used two different vocabularies with the most frequent 7,000 (≥150 exemplars) or 25,000 (≥20 exemplars) words from the training data transcripts. We have also experimented with 90k vocabulary CTC word models, and note that the bidirectional model gives a 25% lower WER than the unidirectional model.