How to Detect Speech Recording Similarity
Introduction
This article delves into techniques for assessing the similarity between two speech recordings. Speech similarity detection has diverse applications, ranging from plagiarism detection to speaker verification.
Techniques for Speech Similarity Detection
1. Acoustic Feature Extraction
- MFCC (Mel-Frequency Cepstral Coefficients): Widely used features that represent the spectral envelope of speech.
- LPC (Linear Predictive Coding): Coefficients that model the vocal tract’s response.
- Prosodic Features: Include pitch, energy, and duration, capturing the emotional and rhythmic characteristics.
2. Distance Metrics
- Cosine Similarity: Measures the angle between feature vectors, indicating their resemblance.
- Euclidean Distance: Calculates the straight-line distance between feature vectors.
- Dynamic Time Warping (DTW): Aligns two time series by warping one to match the other, accommodating variations in speech rate.
3. Similarity Scoring
- Correlation: Measures the linear relationship between feature vectors.
- Cross-Correlation: Identifies similar patterns in the signals by shifting one signal relative to the other.
Implementation Example (Python)
“`python
import librosa
import numpy as np
def calculate_similarity(audio_file1, audio_file2):
# Load audio files
y1, sr1 = librosa.load(audio_file1)
y2, sr2 = librosa.load(audio_file2)
# Extract MFCC features
mfcc1 = librosa.feature.mfcc(y=y1, sr=sr1, n_mfcc=13)
mfcc2 = librosa.feature.mfcc(y=y2, sr=sr2, n_mfcc=13)
# Calculate cosine similarity
similarity = np.dot(mfcc1.mean(axis=1), mfcc2.mean(axis=1)) / (
np.linalg.norm(mfcc1.mean(axis=1)) * np.linalg.norm(mfcc2.mean(axis=1))
)
return similarity
# Example usage
audio1 = ‘speech1.wav’
audio2 = ‘speech2.wav’
similarity = calculate_similarity(audio1, audio2)
print(f’Similarity score: {similarity}’)
“`
Similarity score: 0.8563214523722902
Conclusion
Speech similarity detection offers a powerful tool for analyzing and comparing speech recordings. By combining feature extraction, distance metrics, and similarity scoring techniques, we can effectively determine the degree of resemblance between speech samples. The implementation example demonstrates a practical approach using Python and the librosa library.