Punjabi Children Speech Recognition System Under Mismatch Conditions Using Discriminative Techniques

Abstract

It is a very difficult challenge to recognize children’s speech on Automatic Speech Recognition (ASR) systems built using adult speech. In such ASR tasks, a significant deteriorated recognition efficiency is observed, as noted by several earlier studies. It is primarily related to the significant inconsistency between the two groups of speakers in the auditory and linguistic attributes. One of the numerous causes of conflict found is that the adult and child speaker vocal organs are of substantially different dimensions. Discriminatory approaches are noted for dealing extensively with the effects emerging from these differences. Specific parameter variations have been introduced with boosted parameters and iteration values to achieve the optimum value of the acoustic models boosted maximum mutual information (bMMI) and feature-space bMMI (fbMMI). Experimental results demonstrate that the feature space discriminative approaches have achieved a significant reduction in the Word Error Rate (WER). This is also shown that fbMMI achieves better performance than the bMMI and fMMI. Recognition of children and the elderly will need even more studies if we are to examine these age groups features in existing and future speech recognition systems.