Neural Network Based Gujarati Speech Recognition for Dataset Collected by in-ear Microphone

This paper addresses different configurations of two layers and three layers neural network approach for the low resource language like Gujarati. The speech data are collected with the in-ear microphone compare to conventional microphone system and results are compared. Different end point detection algorithms are also tested to remove an unwanted silence portion where maximum chances of noise take place. Word boundary detection is used to separate out the different words form sentences. Detected words are then passed to the feature extraction block. Feature extractions are done with the help of the Mel-Frequency Cepstral Coefficients (MFCCs) and Real Cepstral Coefficients (RC). Results are tested and compared to them. Two layers and three layers neural networks approach are used for the classification.