Statistical analysis of features and classification of alphasyllabary sounds in Kannada language
Automatic speech recognition (ASR) for a given audio file is a challenging task due to the variations in the type of speech input. Variations may be the environment, language spoken, emotions of the speaker, age/gender of speaker etc. The two main steps in ASR are converting the audio file into features and classifying it appropriately. Basic unit of speech sound is phoneme and the list of such phoneme is language dependent. In Indian languages, basic unit of language is known as Akshara i.e the alphabet. It is known to be an alphasyllabary unit. In our work, we have analyzed the behavior of the acoustic features like, Mel frequency cepstral coefficients and linear predictive coding for various aksharas using techniques like, visualization, probability density function (pdf), QQ plot and F-ratio. The classifiers, support vector machine (SVM) and hidden Markov model (HMM) are used for classifying the recorded audio into corresponding aksharas. We have also compared the classification performance of HMM and SVM.
statistical analysis alphasyllabary kannada language speech recognition support vector machine hidden markov model mel frequency cepstral coefficients (mfcc) linear predictive coding (lpc) vector quantization (vq)