A speaker invariant speech recognition technique using HFCC features in isolated Hindi words

摘要

A speaker invariant speech recognition system is proposed by analysing the characteristics of speech signal. The distinctive features are derived from the speech data using discrete wavelet transforms (DWT) and human factor cepestral coefficient (HFCC) technique. This HFCC technique provides an immense impact on signal decoupling process for adjusting parameters in noise smoothing and spectral resolution. We have created a speech repository of 12 isolated Hindi words. The principal component analysis (PCA) is applied on speech features obtained from HFCC analysis in order to reduce the dimension of feature space. We have applied Bayes' decision rule for classification with multivariate normal distribution which follows the class conditional probability density function for each training classes. The performance of the classifier has been evaluated by calculating the misclassification error probability. Experimental results of proposed method are analysed and compared with the existing methods like MFCC with DWT, MFCC with PCA, DWT with PCA, etc. We have achieved promising classification results using HFCC-based speech features for speaker invariant speech identification system.