A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications
Table 1: ASR performance without front-end (no FE) and with front-end (FE). Numbers in parenthesis with ↓ denote reduction in the error rate.
['[EMPTY]', '[EMPTY]', 'Google no FE', 'Google FE', 'Google FE', 'IBM no FE', 'IBM FE', 'IBM FE', 'ASpIRE no FE', 'ASpIRE FE', 'ASpIRE FE']
[peddinti2015jhu, aspirelink] with and without our proposed front-end, when tested with laughter-speech (speech perturbed with emotion) and creaky-speech (speech perturbed with voice-quality). The performance is evaluated in terms of % Word Error Rate (%WER) and % Sentence Error Rate (%SER). Lower values of WER and SER indicate better performances. It can be observed that modeling both, spectral and aperiodic components (i.e., MFB + APs) performs better than modeling only MFBs in the proposed front-end. Absolute reductions of 14.9%, 8%, 21.0% in WER and 16.3% , 3.4%, 3.4% in SER, are achieved for Google ASR, IBM ASR and ASpIRE ASR, respectively, when our proposed front-end (MFBs+APs) is used to convert laughter-speech to normal speech. Similarly, absolute reductions of 11.0%, 7.9%, 7.9% in WER and 22.2%, 2.8% 11.1% in SER are obtained for Google ASR, IBM ASR and ASpIRE ASR, respectively, when our proposed front-end (MFBs+APs) is used to convert creaky-speech to normal speech. The DeepSpeech model was trained on 1000 hours of LibriSpeech data and did not use a language model for decoding.