Speaker Recognition Using Wavelet Neural Network and Probabilistic Neural Network (PNN)

Speaker recognition represents a crucial application in speech signal processing, aiming to identify or verify speakers' identities through analysis of vocal characteristics. Methods based on Wavelet Neural Networks (WNN) and Probabilistic Neural Networks (PNN) demonstrate exceptional performance in this task, particularly excelling in nonlinear feature extraction and classification.

Wavelet transform effectively extracts time-frequency features from speech signals, overcoming limitations of traditional Fourier transforms in analyzing non-stationary signals. Through wavelet decomposition, key features like fundamental frequency and formants can be captured at multiple resolutions. These features are subsequently fed into PNN for classification - typically implemented using PyWavelets for decomposition and custom neural network layers for feature processing.

PNN, built upon Bayesian decision theory, offers a simple network structure with superior classification performance. It makes classification decisions by calculating probability density functions (PDFs) between input features and different categories, particularly suitable for small-sample classification problems like speaker recognition. The implementation often involves radial basis functions and Parzen window estimation for probability density calculation.

The integrated approach combining wavelet analysis and PNN generally follows these implementation steps: Preprocessing: Speech signals undergo noise reduction, framing, and windowing (commonly using Hamming windows) to enhance feature extraction accuracy. Code implementation typically involves librosa or PyAudio for audio processing. Feature Extraction: Wavelet transform decomposes speech signals to extract multi-scale features like wavelet coefficient energy or statistical properties. Common implementations use discrete wavelet transform (DWT) with Daubechies or Haar wavelets. Classification: Extracted features are input to the PNN network, which calculates probabilities for different speaker categories using parallel pattern layers and summation units, ultimately outputting recognition results through competitive output layers.

This methodology maintains robust recognition performance even under high-noise environments or short-duration speech conditions, making it suitable for practical applications like security systems and intelligent customer service platforms.