Classification of Speech Using MATLAB and K-Nearest Neighbour Model: Aid to the Hearing Impaired
Balvin Thorpe, Trae Dussard · 2018
Throughout the history of human existence, speech has been the most dominant and convenient method of communication with each other. However, many problems can arise due to speech miscommunication. Issues such as: noisy communication medium, word pronunciations, speakers' accents, language barriers, among others. These issues can affect the understanding and interpretation the spoken words. This can be bad for the normal hearing listeners, but it is even worse for the listeners with hearing impairments. Hearing aids are used to help persons with hearing losses. One problem with hearing aids is that they generally amplify all signals that are inputted through their microphones. Digital hearing aids, however, improve hearing by reducing background noise and significantly improve sound qualities. However, hearing aids do not improve overall hearing for the hearing impaired, when the challenge for some, especially the elderly, is recognizing high frequency sounds which typically are low energy sounds as most consonants are. In this paper, speech was classified using K-Nearest Neighbour (K-NN), a non-parametric method, so as to identify vowels differently from consonants. The KNN model was built in MATLAB by parsing the phoneme data in the TIMIT training database, and generating the corresponding Mel Frequency Cepstrum Coefficients (MFCC) for each phoneme. The trained K-NN classifier model was generated in MATLAB from the phonemes MFCC generated. The classifier was then tested using the TIMIT test database, to determine the performance. Results showed that our classifier worked with an accuracy that ranged between 84%to 96%. These results indicated that the proposed approach can be used to classify vowels and consonants of spoken words and hence can help to improve hearing aids' output quality to the general hearing impaired community.