Acoustic parameters for automatic detection of nasal manner

Abstract

Of all the sounds in any language, nasals are the only class of sounds with dominant speech output from the nasal cavity as opposed to the oral cavity. This gives nasals some special properties including presence of zeros in the spectrum, concentration of energy at lower frequencies, higher formant density, higher losses, and stability. In this paper we propose acoustic correlates for the linguistic feature nasal. In particular, we focus on the development of Acoustic Parameters (APs) which can be extracted automatically and reliably in a speaker independent way. These APs were tested in a classification experiment between nasals and semivowels, the two classes of sounds which together form the class of sonorant consonants. Using the proposed APs with a support vector machine based classifier we were able to obtain classification accuracies of 89.53%, 95.80% and 87.82% for prevocalic, postvocalic and intervocalic sonorant consonants respectively on the TIMIT database. As an additional proof to the strength of these parameters, we compared the performance of a Hidden Markov Model (HMM) based system that included the APs for nasals as part of the front-end, with an HMM system that did not. In this digit recognition experiment, we were able to obtain a 60% reduction in error rate on the TI46 database.