Combining Data Augmentations for CNN-Based Voice Command Recognition

This paper presents combining two data augmentation methods involving speed perturbation and room impulse response reverberation for the purpose of improving the generalization capability of convolutional neural networks when used for voice command recognition. Speed perturbation generates voice command variations caused by shorter or longer time durations of commands spoken by different speakers. Room impulse response reverberation generates voice command variations caused by reflected sound paths. The combination of these two augmentation methods is presented in this paper by examining a public domain dataset of voice commands. The experimental results based on the performance metric of word error rate indicate the improvement in voice command recognition rates when combining these data augmentation methods relative to using each augmentation method individually.