Word boundary detection based on suprasegmental features: A case study on Bangla speech

Abstract

This paper proposes a method for detecting word boundaries in continuous speech signal for Standard Colloquial Bengali (SCB), commonly referred to as Bangla. Bangla is a bound stress language with stress on the first syllable. Stress introduces its signature on the supra-segmental parameters of the speech signal, which may help to detect the word boundary in the continuous speech signal. The parameters used in this present study are: (1) Difference of the nucleus vowel duration across the syllable boundary, (2) Difference of the normalized nucleus vowel power across the syllable boundary, (3) Normalized F0 difference across the syllable boundary, (4) Difference of the average normalized F0 across the syllable boundary, (5) Difference of the normalized maximum periodic power of nucleus vowels across the syllable boundary, (6) Onset duration of the nucleus vowel. Altogether 225 sentences spoken by five native Bangla informants of both the sexes, in the age group of 20–50 years in normal laboratory environment are used in this study. These sentences contain 2734 syllables and 1103 words, sentence terminal words being excluded. A recognition score of 87.8% with a classifier, based on a distance function, weighted by inverse of variance is reported. Both speaker dependent as well as speaker independent studies are included.