Improved phone recognition on TIMIT using formant frequency data and confidence measures

This paper presents a novel approach to integration of formant frequency and conventional MFCC data in phone recognition experiments on TIMIT. Naive use of format data introduces classification errors if formant frequency estimates are poor, resulting in a net drop in performance. However, by exploiting a measure of confidence in the formant frequency estimates, formant data can contribute to classification in parts of a speech signal where it is reliable, and be replaced by conventional MFCC data when it is not. In this way an improvement of 4.7% is achieved. Moreover, by exploiting the relationship between formant frequencies and vocal tract geometry, simple formant-based vocal tract length normalisation reduces the error rate by 6.1% relative to a conventional representation alone.