Spectral and prosodic features-based speech pattern classification

Abstract

Speech pattern produced by individuals are unique. This uniqueness is due to the accent influenced by individual's native dialect. Prior knowledge of spoken dialect provides valuable information for speaker profiling and incorporating them in the decision parameter can improve the system performance. In this paper, an auto-associative neural network model has been proposed to model intrinsic characteristics of speech features for dialect classification. This paper highlights the sufficiency of few spectral and prosodic features for identification of Hindi dialects. Experimental results show that system performance is the best when both spectral and prosodic features are combined to use as input. In the presence of noise, performance of a conventional ASR starts to degrade. The NOISEX-92 database is used to add white noise to the recorded utterances in the range of 0 dB to 20 dB. This paper evaluates the dialect classification system's performance for SNRs in this range.