Bisayan Dialect Short-time Fourier Transform Audio Recognition System using Convolutional and Recurrent Neural Network

Abstract

Speech is a form of oral communication that reinforces thoughts and ideas that have general purpose and meaning. In the Philippines, Filipinos can speak at least three languages. English, Filipino, native language. The Philippine government says the Philippines has more than 150 regional native languages, one of which he says is Cebuano. This research aims to implement automatic speech recognition (ASR) specifically for the Bisayan dialect, and researchers use machine learning techniques to create and operate the system. ASR has served its purpose in recent years not only in the official language of the Philippines, but also in various foreign languages. The required datasets were collected throughout the study to train and build the models selected for the speech recognition engine. Audio files are recorded in waveform file format and contain Visayan phrases and sentences. Audio was captured through hours of recorded audio and process using Tensorflow short time Fourier transform (STFT) algorithm to ensure the accurate representation. In order to analyze the audio data, the recordings were specially converted to digital format, specifically .wav and making it sure all records are uncorrupted with only one channel, and finally have a sample rate of 22050kHz. A data mining process was carried out by integrating CNN layers, dense layers, and RNNs to predict the transcription of speech input using multiple layers that determine the output of the speech data. The researchers used the JiWER Python library, which was used in parallel when evaluating WER. This is because the trained scripted data set contains at least 500 time recordings totaling 61.78 minutes. Overall, the WER output is at best 99.53% and the percentage of records used is acceptable.