Inverse Filtering Based Feature for Analysis of Vowel Nasalization

Abstract

Vowel nasalization is present in almost every Indic languages. Detection of vowel nasalization can enhance the accuracy of Automatic Speech Recognition (ASR) systems designed for Indian languages. It also provides significant clinical information about the vocal tract. In pursuit of developing some acoustic parameters for detection of nasalized vowels, most researchers have extensively analyzed its spectral domain characteristics. In this work, we have used an inverse filtering based technique to develop a novel feature, which represents the amount of nasalization present in a vowel. The invariability of nasal filter for different nasalized vowels and addition of oral and nasal speech after radiation has been exploited to find out this feature. As the feature gives information about the amount of nasalization, this can be used for detection of vowel nasalization as well as for clinical purposes. Statistical analysis of the feature has been done in this work. The statistical analysis shows that the feature has good separability for oral vowels and nasalized vowels.