A Wavelet Transform Based Scheme to Extract Speech Pitch and Formant Frequencies

TL;DR

This paper introduces wavelet transform-based methods for extracting speech pitch and formant frequencies, demonstrating improved accuracy and noise robustness over traditional cepstrum techniques in speech processing applications.

Contribution

It proposes two novel wavelet-based approaches for feature extraction and compares their effectiveness with cepstrum methods, highlighting advantages in accuracy and noise insensitivity.

Findings

Wavelet transform methods outperform cepstrum in accuracy.

Wavelet approaches show robustness to noise.

Proposed methods are effective for speech feature extraction.

Abstract

Pitch and Formant frequencies are important features in speech processing applications. The period of the vocal cord's output for vowels is known as the pitch or the fundamental frequency, and formant frequencies are essentially resonance frequencies of the vocal tract. These features vary among different persons and even words, but they are within a certain frequency range. In practice, just the first three formants are enough for the most of speech processing. Feature extraction and classification are the main components of each speech recognition system. In this article, two wavelet based approaches are proposed to extract the mentioned features with help of the filter bank idea. By comparing the results of the presented feature extraction methods on several speech signals, it was found out that the wavelet transform has a good accuracy compared to the cepstrum method and it has no sensitivity to noise. In addition, several fuzzy based classification techniques for speech processing are reviewed.