On the Role of Linear, Mel and Inverse-Mel Filterbank in the Context of Automatic Speech Recognition

In the context of automatic speech recognition (ASR), the power spectrum is generally warped to the Mel-scale during front-end speech parameterization. This is motivated by the fact that, human perception of sound is nonlinear. The Mel-filterbank provide better resolution for low-frequency contents while a greater degree of averaging happens in the high-frequency range. The work presented in this paper aims at studying the role of linear, Mel and inverse-Mel filterbanks in the context of speech recognition. It is well known that, when speech data is from high-pitched speakers like children, there is a significant amount of relevant information in the high-frequency region. Hence, down-sampling the information in that range through Mel-filterbank reduces the recognition performance. On the other hand, employing inverse-Mel or linear-filterbanks are expected to be more effective in such cases. The same has been experimentally validated in this work. To do so, an ASR system is developed on adults' speech and tested using data from adult as well as child speakers. Significantly improved recognition rates are noted for children's as well adult females' speech when linear or inverse-Mel filterbank is used. The use of linear filters results in a relative improvement of 21% over the baseline.