Recognition of Children Punjabi Speech using Tonal Non-Tonal Classifier
Taniya Hasija, Virender Kadyan, Kalpna Guleria
Abstract
Tonal Languages Automatic Speech recognition (ASR) systems are an area of interest for researchers as it is a challenging research domain. However, the main objective is to get an ASR system that has high performance whether the language used is tonal or non-tonal. But tonal languages always have degraded results as compared to non-tonal languages. Punjabi is a tonal language and tonality has led to many challenges in the designing of the Punjabi ASR system. It is an important research area to develop a Punjabi ASR system that is able to tackle the tonality of the language. In this paper, tonal and non-tonal classification is done to extract the prosodic features, which in turn enhance the word recognition rate for tonal languages. A prosodic feature has pitch related features, which can effectively understand the tone of the word and has high accuracy in recognition of words. Extracted prosodic features are fed to the ASR system individually and then later in combinations.. Results of prosodic feature's combination with Mel Frequency Cepstral Coefficients (MFCC) are compared with the MFCC based ASR results. The performance of the system is analyzed and it is observed that WER is reduced by implementing prosodic features with Relative Improvement (RI) of14%.The results are compared with the baseline result and the performance of the system is analyzed.