Isolated digit filipino speech recognition through spectrogram image classification: Towards application in a disaster preparedness participatory toolkit

In this paper, we present our work on isolated digit speech recognition: by classifying spectrogram images and for use in a disaster preparedness participatory toolkit. To achieve higher inclusivity, we included a voice component for a wider coverage of respondents especially those who have low literacy and those vision impaired individuals. Our methodology is through speech recognition which is a deviation from usual approaches which normally work on acoustic coefficients and features. As our initial test bed, we focused on the Filipino language - a member of the Malayo-Polynesian language family and is the national language in the Philippines. Our data covers 4,297 utterances of the Filipino digits 0 to 9 collected from 262 speakers, and divided the data into 3 parts: 70% for training, 20% for testing, and 10% for validation. We applied short-time Fourier transform on our training data and we used convolution neural networks in MatLab to classify the spectrogram images. The lowest accuracy rate during our tests is 93.02%. Analyses of the results show that background noises are the cause of the misclassified utterances which will further discussed on this paper. While the results are promising, the work can be extended to include closely related languages.