Spoken Digit Recognition using the k-Nearest-Neighbor method and Mel Frequency Cepstral Coefficients

This study investigates the utilization of the k-nearest-neighbor algorithm within the framework of machine learning for speech recognition applications. The AudioMNIST dataset is used for performing the evaluations in which the model predicts the spoken digit, namely from 0 to 9. Two different training-to-test percentage splits of the dataset are used, 70%-30% and 80%-20%, while the k parameter ranges from 1 to 12. To better adapt the predic-tion model, the Mel-frequency cepstrum coefficients are extracted from each audio sample, and the 13 filters are averaged over 25 ms frame windows with 10 ms frame overlap. In both training-to-test configurations the value for the k parameter that obtained the highest accu-racy (> 95%) is k=5, while the easiest to predict digits was “7†. These findings underscore the efficacy of k-nearest-neighbor in speech recognition tasks and highlight the importance of parameter selection and feature extraction techniques in optimizing model performance. Further exploration of kNN's applicability in diverse speech recognition contexts holds promise for advancing the field's understanding and practical implementations.