BP neural network speech recognition algorithm based on momentum term and its MATLAB source code

BP neural network speech recognition algorithm based on momentum term and its MATLAB source code

Neural networks have a wide range of applications in the field of speech recognition. Among them, neural networks based on the Backpropagation (BP) algorithm are widely used in speech recognition tasks. This article will introduce a BP neural network speech recognition algorithm based on momentum terms, and provide the corresponding MATLAB source code.

1. Algorithm principle
   The speech recognition algorithm based on BP neural network is mainly divided into three steps: forward propagation, error calculation and weight update.

1.1 Forward propagation
In the forward propagation process, the input speech signal is fed to the input layer in the neural network and then passed to the output layer through the hidden layer. Each neuron has an activation function to process the input signal and pass the output to the next layer.

1.2 Error calculation
Error calculation refers to calculating the error between the neural network output and the actual label. A commonly used error calculation method is Mean Square Error (MSE). By comparing the network output with the actual labels, the error value can be obtained.

1.3 Weight update
In the weight update stage, the error is back-propagated back to the hidden layer and input layer to adjust the connection weight between each neuron in the network. During the propagation process, the gradient descent method is used to update the weights. In order to speed up the convergence and reduce the oscillation, the momentum term can be introduced. The momentum term adjusts the current weight update by taking into account the direction and magnitude of the previous weight update.

2. MATLAB code implementation
   The following is an example of MATLAB source code based on BP neural network speech recognition with momentum terms:

% 设置神经网络参数
inputSize = 10