IMPLEMENTASI KUNCI BERBASIS SUARA MENGGUNAKAN METODE MEL FREQUENCY CEPSTRAL COEFFICIENT (MFCC)

Pada dasarnya setiap individu menghasilkan suara yang berbeda-beda, walaupun seseorang dapat menirukan suara tersebut namun suara yang dihasilkan tidak identik dengan suara yang ditiru. Sistem biometrik adalah sistem untuk melakukan identifikasi dengan menganalisa karakteristik fisik dan perilaku. Tugas Akhir ini membuat suatu sistem keamanan suara berbasis mikro komputer yang diimplementasikan menjadi kunci. Tugas Akhir ini menggunakan metode MFCC sebagai ekstraksi ciri dan K-NN sebagai klasifikasi cirinya. Pada penelitian Tugas Akhir ini telah berhasil membuat sistem pengenalan pembicara dengan tingkat akurasi terbaik sebesar 87.5% dan 1.80277 detik dengan menggunakan K = 5 dalam implementasi pembuka kunci menggunakan suara. Kata kunci : Mel-Frequency Cepstral Coefficient (MFCC), K-Nearest Neighbor (K-NN), biometrik suara, pengenalan pembicara

Acoustic Analysis of Nigerian English Vowels Based on Accents

Accent has been widely acclaimed to be a major source of automatic speech recognition (ASR) performance degradation. Most ASR applications were developed with native English speaker speech samples not minding the fact that the majority of its potential users speaks English as a second language with a marked accent. Nigeria like most nations colonized by Britain, speaks English as official language despite being a multi-ethnic nation. This work explores the acoustic features of energy, fundamental frequency and the first three formats of the three major ethnic groups of Nigerian based on features extracted from five pure vowels of English obtained from subjects who are Nigerians. This research aimed at determining the differences or otherwise between the pronunciations of the three major ethnic nationalities in Nigeria to aid the development of ASR that is robust to NE accent. The results show that there exist significant differences between the mean values of the pure English vowels based on the pronunciation of the three major ethnics: Hausa, Ibo, and Yoruba. The differences can be explored to enhance the performance of ASR in recognition of NE

A review of Yorùbá Automatic Speech Recognition

Automatic Speech Recognition (ASR) has recorded appreciable progress both in technology and application.Despite this progress, there still exist wide performance gap between human speech recognition (HSR) and ASR which has inhibited its full adoption in real life situation.A brief review of research progress on Yorùbá Automatic Speech Recognition (ASR) is presented in this paper focusing of variability as factor contributing to performance gap between HSR and ASR with a view of x-raying the advances recorded, major obstacles, and chart a way forward for development of ASR for Yorùbá that is comparable to those of other tone languages and of developed nations.This is done through extensive surveys of literatures on ASR with focus on Yorùbá.Though appreciable progress has been recorded in advancement of ASR in the developed world, reverse is the case for most of the developing nations especially those of Africa.Yorùbá like most of languages in Africa lacks both human and materials resources needed for the development of functional ASR system much less taking advantage of its potentials benefits. Results reveal that attaining an ultimate goal of ASR performance comparable to human level requires deep understanding of variability factors

Systematic Review of Machine Learning Approaches for Detecting Developmental Stuttering

A systematic review of the literature on statistical and machine learning schemes for identifying symptoms of developmental stuttering from audio recordings is reported. Twenty-seven papers met the quality standards that were set. Comparison of results across studies was not possible because training and testing data, model architecture and feature inputs varied across studies. The limitations that were identified for comparison across studies included: no indication of application for the work, data were selected for training and testing models in ways that could lead to biases, studies used different datasets and attempted to locate different symptom types, feature inputs were reported in different ways and there was no standard way of reporting performance statistics. Recommendations were made about how these problems can be addressed in future work on this topic