Acoustically grounded word embeddings for improved acoustics-to-word speech recognition
Table 3: Results (% WER) on the SWB/CH evaluation sets. The best result for each data set and each vocabulary size is boldfaced.
['System', 'Vocab 4K', 'Vocab 10K', 'Vocab 20K']
We find that both embedding initialization and regularization improve over the baseline WERs. For the regularized model, the values of λ (tuned on held-out data) are 0.25, 0.25, and 0.5 for the 4K, 10K, and 20K vocabularies. However, all values of λ yield improvements on the held-out set over the baseline. Although outperformed by the embedding-initialized and regularized models, the model trained with a frozen prediction layer (“Frozen”) also consistently outperforms the baseline, with the exception of the 20K CH evaluation. For the 4K vocabulary recognizer, this approach results in an overall absolute WER reduction of 1.4% over the baseline model.