Phoneme Classification in High-Dimensional Linear Feature Domains
TABLE III: Existing error rates obtained in other studies for a range of classification methods on the TIMIT core test set. Results in this paper are most comparable to the GMM baselines.
['[BOLD] Method', '[BOLD] Error [%]']
We see that the best results for acoustic waveform classifiers are achieved around 9 frames, and around 11 frames for PLP without deltas. The PLP+Δ+ΔΔ features are less sensitive to the number of frames with little difference in error from 1 to 13 frames. We can now also assess quantitatively the performance benefit of including the deltas. If we consider the best results obtained for PLP without deltas, 22.4% using 11 frames, with the best for PLP+Δ+ΔΔ, 21.8% with 7 frames, then the performance gap of 0.6% is much smaller than if we were to compare error rates where both classifiers used the same number of frames. Clearly it is not surprising that fewer PLP+Δ+ΔΔ frames are required for the same level of performance as the deltas are a direct function of the neighbouring PLP frames. It is still worth noting that in terms of the ultimate performance on this classification task the error rates with and without deltas are similar. The error rates obtained using the f-average over the five best values of f are 32.1%, 21.4% and 18.5% for acoustic waveforms, PLP and PLP+Δ+ΔΔ respectively. Given the encouraging results from these experiments on a small set of phonemes we progressed to a more realistic task and extended the classification problem to include all phonemes from the TIMIT database. All of the entries show the error for isolated phoneme classification except for the optimum-transformed HMM (THMM) The inclusion of context for the HMM classifiers reduces the error rate from 31.4% to 17.8%. This dramatic reduction suggests that if the other classifiers were also developed to directly incorporate contextual information, significant improvements could be expected.