Isolated Word Speech Recognition using BP Neural Network: MATLAB Speech Simulation Process

The MATLAB simulation process for isolated word speech recognition using BP neural network involves the following key steps:

First, prepare a speech signal dataset containing various word samples. In MATLAB implementation, this typically involves recording or loading audio files using functions like audioread() and organizing them into training and testing sets. Next, perform speech signal preprocessing including noise removal, denoising, and frame segmentation. For implementation, use MATLAB's signal processing toolbox functions such as filter() for noise reduction and buffer() for frame splitting with overlapping windows.

Then, extract speech features where commonly used features include MFCC (Mel-Frequency Cepstral Coefficients) and LPCC (Linear Predictive Cepstral Coefficients). In code implementation, MFCC extraction involves steps like pre-emphasis, framing, windowing, FFT, Mel-filterbank, and DCT transformation using functions like mfcc() from voicebox toolbox or custom implementations. The extracted features serve as input to the BP neural network for training and learning.

During the training process, define appropriate loss functions (typically mean squared error) and optimization algorithms (like gradient descent or backpropagation). The network architecture can be implemented using MATLAB's Neural Network Toolbox with functions like feedforwardnet() and train(), where parameters such as learning rate, number of hidden layers, and activation functions need careful configuration. The training process iteratively adjusts network weights and biases to minimize the error between predicted and actual outputs.

After training completion, the trained BP neural network can be used for recognizing and classifying new speech signals. In MATLAB code, this involves using the sim() or predict() function with the trained network model on test feature vectors. Performance evaluation metrics like accuracy, confusion matrix, and ROC curves can be generated using functions such as confusionmat() and perfcurve().

Through these steps, the MATLAB simulation process using BP neural network enables automatic word recognition. This method finds extensive applications in speech recognition technology research and practical implementations, contributing significantly to advancements in the speech recognition field. The implementation typically includes error handling, parameter tuning loops, and visualization of results using MATLAB's plotting capabilities.