Voice Biometric System - CSU1530 - Shoolini U

Voice Biometric System

1. Introduction to Voice Biometric Systems

Voice Biometric Systems identify or verify individuals based on the unique characteristics of their vocal patterns. These systems analyze features such as pitch, tone, and speech dynamics to authenticate users. Voice biometrics offer a convenient and natural way for security and authentication in various applications.

Applications include:

2. Characteristics of Human Voice

The human voice contains features that are unique to each individual, making it suitable for biometric recognition.

2.1 Physiological Features

Attributes related to the physical structure of the vocal tract:

2.2 Behavioral Features

Attributes related to speaking habits and patterns:

3. Voice Data Acquisition

Capturing high-quality voice recordings is essential for accurate recognition.

3.1 Acquisition Methods

Techniques for collecting voice data include:

3.2 Challenges in Acquisition

Potential issues during voice data capture:

Mitigation strategies include noise reduction techniques and consistent recording setups.

4. Preprocessing of Voice Signals

Preprocessing enhances voice recordings and prepares them for feature extraction.

4.1 Noise Reduction

Removing unwanted sounds from the voice signal:

4.2 Voice Activity Detection

Identifying segments of the recording that contain speech:

4.3 Normalization

Standardizing the voice signal for consistent analysis:

5. Feature Extraction in Voice Biometrics

Extracting distinctive features from the voice signal to create a representative feature vector.

5.1 Short-Term Spectral Features

Analyzing the frequency content of short segments of the voice signal:

MFCC calculation steps:

  1. Divide the signal into overlapping frames.
  2. Apply a window function (e.g., Hamming window) to each frame.
  3. Compute the Fast Fourier Transform (FFT) of each frame.
  4. Map the powers of the spectrum onto the mel scale using triangular filter banks.
  5. Take the logarithm of the filter bank energies.
  6. Compute the Discrete Cosine Transform (DCT) of the log energies.

MFCCs are the resulting coefficients from the DCT.

5.2 Prosodic Features

Capturing long-term characteristics of speech:

5.3 Spectral Dynamics

Analyzing changes in the spectral content over time:

Delta coefficients are computed as:

$$ \Delta c_t = \frac{\sum_{n=1}^N n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^N n^2} $$

6. Matching and Classification

Comparing voice features to identify or verify individuals.

6.1 Distance Metrics

Calculating similarity between feature vectors using:

6.2 Classification Algorithms

Methods for assigning voice data to identities:

6.2 Speaker Modeling with GMM

Creating a model for each speaker using GMMs:

The likelihood of a feature vector \( \mathbf{x} \) is:

$$ p(\mathbf{x}|\theta) = \sum_{i=1}^M w_i \mathcal{N}(\mathbf{x}|\mu_i, \Sigma_i) $$

7. Evaluation Metrics

Assessing the performance of voice biometric systems using statistical measures.

7.1 Equal Error Rate (EER)

The point where the false acceptance rate equals the false rejection rate.

A lower EER indicates better system performance.

7.2 Detection Error Trade-off (DET) Curve

Plots false rejection rate against false acceptance rate on a normal deviate scale.

Helps in visualizing and comparing system performance.

7.3 Receiver Operating Characteristic (ROC) Curve

Plots true positive rate against false positive rate at various thresholds.

Provides insights into the trade-offs between detection and false alarm rates.

8. Challenges in Voice Biometrics

Factors that can affect the accuracy and reliability of voice biometric systems.

8.1 Variability in Speech

Differences in voice due to various factors:

Mitigation strategies include updating voice models and using robust features.

8.2 Environmental Noise

Background sounds can interfere with voice signals.

Approaches:

8.3 Channel Variability

Differences in recording devices and transmission channels.

Solutions:

8.4 Spoofing Attacks

Attempts to deceive the system using recorded or synthetic voices.

Countermeasures:

9. Implementation Example

An example of building a voice biometric system using MFCC for feature extraction and GMM for classification.

9.1 Data Preparation

Steps involved:

  1. Collect Voice Samples: Gather recordings from multiple speakers with labels.
  2. Preprocess Recordings:
    • Apply noise reduction techniques.
    • Perform voice activity detection to isolate speech segments.

9.2 Feature Extraction with MFCC

Extracting MFCC features from voice samples.

import numpy as np
import librosa

def extract_mfcc_features(signal, sample_rate, num_coefficients):
    # Compute MFCCs
    mfccs = librosa.feature.mfcc(y=signal, sr=sample_rate, n_mfcc=num_coefficients)
    # Transpose to get time frames as rows
    mfccs = mfccs.T
    return mfccs

# Example usage
signal, sample_rate = librosa.load('voice_sample.wav', sr=None)
num_coefficients = 13
mfcc_features = extract_mfcc_features(signal, sample_rate, num_coefficients)

Include delta and delta-delta coefficients for capturing dynamics.

9.3 Speaker Modeling with GMM

Training a GMM for each speaker.

from sklearn.mixture import GaussianMixture

def train_gmm(features, num_components):
    # Create and train GMM
    gmm = GaussianMixture(n_components=num_components, covariance_type='diag', max_iter=200)
    gmm.fit(features)
    return gmm

# Example usage
num_components = 16
speaker_models = {}
for speaker_id, features in speaker_features.items():
    gmm = train_gmm(features, num_components)
    speaker_models[speaker_id] = gmm

9.4 Recognition of New Voice Samples

Identifying the speaker of a new voice sample.

def recognize_speaker(mfcc_features, speaker_models):
    scores = {}
    for speaker_id, gmm in speaker_models.items():
        # Compute log-likelihood
        log_likelihood = gmm.score(mfcc_features)
        scores[speaker_id] = log_likelihood
    # Identify the speaker with the highest score
    identified_speaker = max(scores, key=scores.get)
    return identified_speaker

# Example usage
new_signal, new_sample_rate = librosa.load('new_voice_sample.wav', sr=None)
new_mfcc_features = extract_mfcc_features(new_signal, new_sample_rate, num_coefficients)
predicted_speaker = recognize_speaker(new_mfcc_features, speaker_models)
print(f'Identified Speaker: {predicted_speaker}')

9.5 Evaluating the System

Assessing system performance using test samples.

# Test the recognition function
correct = 0
total = len(test_samples)
for true_speaker, sample_path in test_samples.items():
    signal, sample_rate = librosa.load(sample_path, sr=None)
    mfcc_features = extract_mfcc_features(signal, sample_rate, num_coefficients)
    predicted_speaker = recognize_speaker(mfcc_features, speaker_models)
    if predicted_speaker == true_speaker:
        correct += 1

accuracy = correct / total * 100
print(f'Accuracy: {accuracy:.2f}%')

10. Summary

Voice Biometric Systems leverage the unique characteristics of an individual's voice for identification and verification. By understanding the processes of voice data acquisition, preprocessing, feature extraction, and classification, effective voice recognition applications can be developed. Addressing challenges such as variability in speech and environmental noise is crucial for enhancing system performance and reliability.