On using fractal features of speech sounds in automatic speech recognition

The dynamics of air ow during speech production may often result into some small or large degree of turbulence. In this paper, we quantify the geometry of speech turbulence as re ected in the fragmentation of the time signal by using fractal models. We describe an e cient algorithm for estimating the short-time fractal dimension of speech signals based on multiscale morphological ltering and discuss its potential for phonetic classi cation. We also report experimental results on using the shorttime fractal dimension of speech signals at multiple scales as additional features in an automatic speech recognition system using hidden Markov models, which provides a modest improvement in speech recognition performance.