Exploiting Low-Dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
Table 2: Comparison of ASR performance using DNN posteriors and projected posteriors in clean and noisy conditions on Numbers’95 database. RPCA posteriors indicate an ideal enhancement through low-dimensional posterior reconstruction. Breakdown of WER in terms of insertions (Ins), deletions (Del), and substitutions (Subs) has also been shown out of a total of 13967 words in all test utterances.
['[BOLD] SNR', '[BOLD] Posteriors', '[BOLD] WER (%)', '[BOLD] Ins', '[BOLD] Del', '[BOLD] Subs']
To demonstrate the increased robustness in projected posteriors as compared to the DNN posteriors, we also compared their performance in noisy conditions where artificial white Gaussian noise was added at signal level to the test utterances at signal-to-noise (SNR) ratios of 10 dB, 15 dB and 20 dB. DNN trained on clean speech is used for computing posteriors from noisy test spectral features so that the artificially added noise acts as an unseen variation in the data for DNN. WER of 2.6% using DNN posteriors ( “Rank” 36.6) reduces to a WER of 2.2% using projected posteriors (“Rank” 11.9) i.e. a relative improvement of 15.4%, and when RPCA posteriors (“Rank” 7.6) are used, it is reduced to a mere 0.4%. Since RPCA based low-rank reconstruction of posteriors has been done using ground truth senone alignment, ASR performance in this case is the best case scenario and demonstrates the scope of improvement possible even after DNN based acoustic modeling.