Exploring Discrete Wavelet Transforms for Bimodal Speech Recognition

Abstract

Discrete Wavelet Transforms (DWTs) provide time–frequency representations that are well suited for nonstationary signals such as speech. This study presents a comparison of four wavelet families (Daubechies, Symlets, Coiflets, and Biorthogonal) for bimodal automatic speech recognition across two speech modes (normal and whispered). Experiments use the Whi-Spe database comprising ten speakers (five female and five male). A Dynamic Time Warping (DTW) back-end performs sequence alignment and recognition. Results are reported via summary tables, histograms, and confusion matrices and reveal systematic differences among the wavelet families, identifying the most effective transform for bimodal recognition. These findings provide practical guidance for selecting wavelet-based front ends in whisper-robust automatic speech recognition (ASR) systems.