Phoneme Classification Using Temporal Tracking of Speech Clusters in Spectro-temporal Domain

This article presents a new feature extraction technique based on the temporal tracking of clusters in spectro-temporal features space. In the proposed method, auditory cortical outputs were clustered. The attributes of speech clusters were extracted as secondary features. However, the shape and position of speech clusters change during the time. The clusters temporally tracked and temporal tracking parameters were considered in secondary features. The new architecture was proposed for phoneme classification by a combining classifier using both tracked and energy-based features. Clustered based spectro-temporal features vectors were used for the classification of several subsets of TIMIT database phonemes. The results show that the phoneme classification rate was improved Using tracked spectro-temporal features. The results were improved to 78.9% on voiced plosives classification which was relatively 3.3% higher than the results of non-tracked spectro-temporal feature vectors. The results on other subsets of phonemes showed good improvement in classification rate too.