Learning Robust and Multilingual Speech Representations
Table 4: Sample efficiency experiments with the TDNN trained and evaluated on LibriSpeech. The results are word error rate on the LibriSpeech development and evaluation sets. 10% vs. 100% indicates the amount of training data used. The section in + LM decoding contain results with beamsearch decoding with a 4-gram language model. The underlined (OpenSeq2Seq) scores are taken from public benchmarks.888https://nvidia.github.io/OpenSeq2Seq/html/speech-recognition/wave2letter.html
['[EMPTY]', 'LibriSpeech dev-clean', 'LibriSpeech dev-clean', 'LibriSpeech dev-other', 'LibriSpeech dev-other', 'LibriSpeech test-clean', 'LibriSpeech test-clean', 'LibriSpeech test-other', 'LibriSpeech test-other']
We see that even if the speech recognition models have a large number of parameters and are trained on plenty of supervised data, the learned representations still provide significant improvements. The pattern continues to hold if we use beam search decoding with a language model.