Linear Discriminant Analysis (LDA) can be used as a technique for feature reduction and classification in speech recognition tasks. Below is a simplified MATLAB code snippet that demonstrates how to use LDA for isolated word classification in a speech recognition system. This example assumes that you have preprocessed your audio signals and extracted features such as MFCCs (Mel-Frequency Cepstral Coefficients) which are commonly used in speech recognition.
Please note that for a complete speech recognition system, you would need additional steps including preprocessing (noise reduction, endpoint detection), feature extraction, and post-processing (language modeling). The following code focuses on the classification step using LDA
% Assuming you have the following variables:
% features - an NxD matrix where N is the number of samples and D is the number of features
% labels - an Nx1 vector containing the class labels for each sample
% Split the dataset into training and testing sets
cv = cvpartition(labels, 'HoldOut', 0.3); % Hold out 30% of the data for testing
trainFeatures = features(cv.training,:);
trainLabels = labels(cv.training);
testFeatures = features(cv.test,:);
testLabels = labels(cv.test);
% Train LDA classifier
ldaClassifier = fitcdiscr(trainFeatures, trainLabels, 'DiscrimType', 'linear');
% Predict labels for the test set
predictedLabels = predict(ldaClassifier, testFeatures);
% Calculate accuracy
accuracy = sum(predictedLabels == testLabels) / length(testLabels);
fprintf('Accuracy: %.2f%%\n', accuracy * 100);
% Confusion matrix
confMat = confusionmat(testLabels, predictedLabels);
disp(confMat);
% Optionally, visualize the confusion matrix
figure;
confusionchart(confMat);
title('Confusion Matrix for LDA Classifier');
In the above code:
- features should be a matrix where each row corresponds to the feature vector of a speech sample (e.g., MFCCs of a spoken word).
- labels should be a vector that contains the class labels for each speech sample. The class labels could be numerical identifiers for each word in your isolated word recognition task.
- cvpartition is used to split the data into training and testing sets.
- fitcdiscr is the MATLAB function to train the LDA classifier.
- predict is used to classify the test set based on the trained LDA model.
- The accuracy is computed by comparing the predicted labels with the ground truth for the test set.
- confusionmat creates a confusion matrix, which can be visualized using confusionchart to better understand the classifier's performance.
