Speech Feature Enhancement based on Time-frequency Analysis

Abstract

Time-frequency analysis (TFA) is a powerful method to exploit the hidden information of signals, including speech signals. Many techniques in this group were invented and developed to capture the most crucial stationary feature. However, human speech is not stable, and it contains some non-stationary elements. This work aims to design a new algorithm via the TFA technique to extract the trends and changes inside the speech signal in the time-frequency (TF) plane. We design a new algorithm to create a set of atoms for the signal transform, which can analyze the signal in many different view directions via Poly-Linear Chirplet Transform (PLCT). After processing the signal, the proposed method returns a multichannel output in which each channel results from a particular Linear Chirplet Transform (LCT). The feature then is combined with the MFCC feature to form the final representation. Although the size for speech representation rises, our extracted feature contains rich-meaning information to improve the recognition results compared to other features in gender recognition, dialect recognition, and speaker recognition.