Speaker Identification using MFCC feature and VQ training

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Speaker Identification using MFCC feature and VQ training

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Speaker identification is a fascinating area of voice recognition that focuses on distinguishing individuals based on their unique vocal characteristics. One effective approach involves using Mel-Frequency Cepstral Coefficients (MFCC) as audio features and Vector Quantization (VQ) for training and classification.

MFCC Feature Extraction: MFCCs are widely used in speech processing because they mimic the human auditory system’s response. The process involves converting the audio signal into a spectrogram, applying a Mel filter bank to emphasize perceptually relevant frequencies, and then computing the cepstral coefficients. These coefficients effectively represent the speaker’s vocal tract characteristics, making them ideal for identification tasks.

Vector Quantization (VQ) Training: VQ simplifies speaker modeling by clustering MFCC feature vectors into a smaller set of representative centroids (a codebook). Each speaker’s voice is encoded into a unique codebook, which serves as a compact model for comparison. During testing, an unknown voice sample’s MFCCs are matched against stored codebooks using a distance metric (e.g., Euclidean distance), and the closest match identifies the speaker.

Implementation in MATLAB: A well-structured MATLAB implementation would involve: Preprocessing the audio signal (e.g., noise reduction, framing). Extracting MFCC features for each frame. Training a VQ codebook per speaker via clustering algorithms like LBG (Linde-Buzo-Gray). Comparing test samples against stored codebooks for identification.

This method is efficient for small to medium-sized speaker datasets and provides a solid foundation for more advanced techniques like Gaussian Mixture Models (GMMs) or Deep Learning-based systems.