Punjabi Speech Syllable Segmentation Using Vowel Onset Point Identification - S, Kaur; MK, Gill (International Journal of Computer Sciences and Engineering, 2019)
Speech Recognition has been a wide area of research for a long time now. Researchers have been putting a lot of efforts and devised different methods for the same. For Speech Recognition system, speech signal is divided or segmented into some acoustic units like phonemes, syllables and word which will reduces the search space for unwanted signal or noise. This research work aims at developing an Automatic Speech Segmentation algorithm for Punjabi language which segments the signal into syllabes. For Automatic Speech Syllable Segmentation, a proposed technique detects the syllable boundaries using gamma tone filter and oscillator. In this proposed technique, valley picking picks the valley of the signal and gives the onset of the speech signal. Results of proposed technique was compared with the existing method which takes less time. After that Automatic Speech Classification algorithm classifies the signal into two classes either native or non native. For this, system had been trained using Artificial Neural Network (ANN) for estimating the parameter of Native and Non-Native spekers using Mel Frequency Cepstrum Coefficients (MFCCs) for feature extraction. The whole work was performed in Matlab2016a and the results generated as output with high accuracy.