Speech Emotion Recognition Using Dynamic Time Warping (DTW)

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Resource Overview

A MATLAB-based implementation of speech emotion recognition leveraging Dynamic Time Warping (DTW) algorithm for effective emotion classification through acoustic signal analysis.

Detailed Documentation

This paper presents a DTW-based speech emotion recognition methodology that determines emotional differences by computing Dynamic Time Warping distances between speech signals. The approach has been validated in MATLAB environment, demonstrating its effectiveness in emotion classification. The implementation typically involves extracting acoustic features (such as MFCCs or prosodic features) from speech samples, then applying DTW algorithm to align and compare feature sequences with emotion-specific templates. Key MATLAB functions like dtw from Signal Processing Toolbox or custom DTW implementations with path constraints can be utilized for optimal time alignment. Additionally, the paper provides an overview of DTW operational requirements, including feature normalization procedures and template database creation, to facilitate better understanding and practical application of the method.

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