Speaker Recognition using Gaussian Mixture Models (GMM)

Overview

This project focuses on speaker recognition using Gaussian Mixture Models (GMM), a probabilistic model commonly used in speech processing for modeling the distribution of feature vectors extracted from audio signals. The goal is to build a system that can identify and verify speakers based on their unique voice characteristics.

Objectives

• Preprocess and extract relevant features from audio recordings.
• Train Gaussian Mixture Models (GMM) for different speakers.
• Perform speaker identification and verification using GMM-based classification.
• Evaluate model performance and optimize parameters for better accuracy.

Methodology

1. Data Collection & Preprocessing

• Dataset: Collected or used an existing dataset of recorded speech samples from multiple speakers.
• Preprocessing:
  • Converted audio files to a consistent format (e.g., .wav).
  • Removed noise using filtering techniques.
  • Extracted Mel-Frequency Cepstral Coefficients (MFCCs) as feature representations.

2. Feature Extraction

• Used MFCC features, which effectively capture timbral and phonetic characteristics of a speaker's voice.
• Extracted features using Librosa or Python SpeechRecognition libraries.

3. Modeling with GMM

• Trained a separate GMM for each speaker using the extracted MFCC features.
• Used Expectation-Maximization (EM) algorithm to estimate GMM parameters.
• Configured the number of Gaussian components based on speaker variability.

4. Speaker Identification & Verification

• Identification: Compared an unknown speaker’s MFCC features against trained models and assigned the most probable speaker.
• Verification: Compared a claimed speaker's voice against their trained model to confirm identity.

5. Performance Evaluation

• Measured system accuracy using confusion matrix, precision, recall, and F1-score.
• Analyzed the impact of different numbers of Gaussian components on recognition accuracy.

Tools and Technologies

• Programming Language: Python
• Libraries Used:
  • Librosa (for feature extraction)
  • SciPy (for signal processing)
  • Sklearn.mixture (for GMM implementation)
  • NumPy, Pandas (for data handling)
  • Matplotlib, Seaborn (for visualization)

Results and Findings

• Demonstrated successful speaker recognition with GMM-based classification.
• Found that increasing Gaussian components improves accuracy up to a limit.
• Identified MFCCs as effective features for speaker modeling.

Future Improvements

• Integrate Deep Learning models (e.g., GMM-HMM, CNNs, or LSTMs) for improved accuracy.
• Expand the dataset with more speakers and diverse environmental conditions.
• Implement real-time speaker recognition using a streaming audio framework.

Conclusion

This project successfully implemented Speaker Recognition using GMM, demonstrating the effectiveness of probabilistic modeling for voice-based authentication. It provides a foundation for biometric security systems, voice assistants, and forensic applications.

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