Direct Acoustics-to-Word Modelsfor English Conversational Speech Recognition
Table 2: This table shows the Switchboard and CallHome WERs for different initialization schemes for the 300 hour CTC word models. The randomly-initialized model failed to converge.
['[ITALIC] Init. Method', '[ITALIC] SWB', '[ITALIC] CH']
We trained two sets of 640-dimensional GloVe embeddings: one on the 24M word corpus used for training the small LM, and another on the 560M word corpus used for the big LM. We assigned random vectors for the blank symbol and OOV token. We normalized each word vector to have a unit L2 norm and scaled it by 0.1. We also note that GloVe embeddings trained on the bigger 24M+540M word data set yield around 1% improvement in WER over the embeddings trained only on the 24M word set.