Expiratory and inspiratory cries detection using different signals' decomposition techniques

This paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction, and classification. In the first stage, short-time Fourier transform, empirical mode decomposition (EMD), and wavelet packet transform have been considered. In the second stage, various set of features have been extracted, and in the third stage, two supervised learning methods, Gaussian mixture models and hidden Markov models, with four and five states, have been discussed as well. The main goal of this work is to investigate the EMD performance and to compare it with the other standard decomposition techniques. A combination of two and three intrinsic mode functions (IMFs) that resulted from EMD has been used to represent cry signal. The performance of nine different segmentation systems has been evaluated. The experiments for each system have been repeated several times with different training and testing datasets, randomly chosen using a 10-fold cross-validation procedure. The lowest global classification error rates of around 8.9% and 11.06% have been achieved using a Gaussian mixture models classifier and a hidden Markov models classifier, respectively. Among all IMF combinations, the winner combination is IMF3+IMF4+IMF5

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

SPEECH RECOGNITION FOR CONNECTED WORD USING CEPSTRAL AND DYNAMIC TIME WARPING ALGORITHMS

Speech Recognition or Speech Recognizer (SR) has become an important tool for people with physical disabilities when handling Home Automation (HA) appliances. This technology is expected to improve the daily life of the elderly and the disabled so that they are always in control over their lives, and continue to live independently, to learn and stay involved in social life. The goal of the research is to solve the constraints of current Malay SR that is still in its infancy stage where there is limited research in Malay words, especially for HA applications. Since, most of the previous works were confined to wired microphone; this limitation of using wireless microphone type makes it an important area of the research. Research was carried out to develop SR word model for five (5) Malay words and five (5) English words as commands to activate and deactivate home appliances