Recognition of Spoken Bengali Numerals Using MLP, SVM, RF Based Models with PCA Based Feature Summarization

This paper presents a method of automatic recognition of Bengali numerals spoken in noise-free and noisy environments by multiple speakers with different dialects. Mel Frequency Cepstral Coefficients (MFCC) are used for feature extraction, and Principal Component Analysis is used as a feature summarizer to form the feature vector from the MFCC data for each digit utterance. Finally, we use Support Vector Machines, Multi-Layer Perceptrons, and Random Forests to recognize the Bengali digits and compare their performance. In our approach, we treat each digit utterance as a single indivisible entity, and we attempt to recognize it using features of the digit utterance as a whole. This approach can therefore be easily applied to spoken digit recognition tasks for other languages as well