DEEP TRIPHONE EMBEDDING IMPROVES PHONEME RECOGNITION
Table 1: Tri-phoneme and phoneme classification accuracy.
['Training Methods', 'Tri-Phoneme', 'Phoneme']
The performance improvement by HMM+DNN variants based acoustic model over previous HMM+GMM method validates the discriminative strength of DNN based models. On the top of that, both the variants of our proposed DTE based methods, namely, HMM+DTE-LDA+DNN and HMM+DTE-PCA+DNN achieves an absolute improvement of 5.31% / 6.18% and 2.22% / 2.26%, in tri-phoneme / phoneme classification accuracy, respectively. The relative improvements of 8.31% and 6.66% in tri-phoneme / phoneme classification accuracy are achieved by HMM+DTE-PCA+DNN, when compared against all variants of HMM+DNN based baselines.