An improved feature extraction method for Malay vowel recognition based on spectrum delta

Malay speech recognition is becoming popular among Malaysian researchers. In Malaysia, more local researchers are focusing on noise robust and accurate independent speaker speech recognition systems that use Malay language.The performance of speech recognition application under adverse noisy condition often becomes the topic of interest among speech recognition researchers in any languages.This paper presents a study of noise robust capability of an improved vowel feature extraction method called Spectrum Delta (SpD).The features are extracted from both original data and noise-added data and classified using three classifiers; (i) Linear Discriminant Analysis (LDA), (ii) K-Nearest Neighbors (k-NN) and (iii) Multinomial Logistic Regression (MLR). Most of the dependent and independent speaker systems which use mostly multi-framed analysis, yielded accuracy between 89% to 100% for dependent speaker system and between 70% to 94% for an independent speaker. This study shows that SpD features obtained an accuracy of 92.42% to 95.11% using all the four classifiers on a single framed analysis which makes this result comparable to those analysed with multi-framed approach

Malay word pronunciation test application for pre-school children

In Malaysia, many researchers focus on developing independent speaker speech recognition systems that uses Malay Language. Accuracy, noise robustness and processing time are concerns when developing speech therapy systems especially for children. In this study, a Malay word pronunciation test application is developed using Spectrum Delta features and Logistic Regression classification model in an effort to improve Malay word pronunciation for pre-school children aged between 3-6 years old. Results showed that the pronunciation application can assist children to test and improve their Malay word pronunciation

Development of Malay word pronunciation application using vowel recognition

In Malaysia, many researchers focus on developing speaker independent systems for training or articulation therapy or to assist language learners to learn about Malay Language or Bahasa Malaysia.Accuracy, noise robustness and processing time are concerns when developing speech therapy systems.In this study, a Malay word pronunciation test application was developed using the first 3 format and fundamental frequencies in an effort to improve pronunciation in Malay.This application was developed using Matlab and uses a vowel recognition algorithm classified using MLP classification technique.The application was developed and tested on UUM undergraduate students.For vowel classification, when fundamental frequency was added, 3-format feature vowel classification rate increased by 1.55% for male gender and 1.48% for female. When combined both genders, a more significant improvement of 1.71% was seen.The developed pronunciation application test results showed that the pronunciation application can assist in testing and improving their Malay word pronunciation. It was also observed that, vowel /i/, /e/, /o/ and /u/ are often mispronounced due to pronunciation habits

Malay word pronunciation application for pre-school children using vowel recognition

In Malaysia, many researchers focus on developing independent speaker speech recognition systems that use Malay Language or Bahasa Malaysia. Accuracy, noise robustness and processing time are concerns when developing speech therapy systems especially for children. In this study, a Malay word pronunciation test application is developed using Spectrum Delta (SPD) features and Logistic Regression classification model in an effort to improve Malay word pronunciation for pre-school children aged between 3-6 years old.Based on the 6 vowel classification rate, vowel /i/ were found to achieved the highest classification rate of 98.33% and vowel /o/ achieved the worst with 92.29%. Overall classification rate obtained was 95.11%. Results showed that the pronunciation application can assist children to improve their Malay word pronunciation. Vowel /i/, /e/, /o/ and /u/ are often mispronounced due to pronunciation habits

A Review on Technique in Managing Oil Palm Plantation towards a Digitalized Online 3D Application

The oil palm industry has been well known as the backbone in Malaysian agriculture and still maintain as the main commodity exports. The research on oil palm industry has been growing gradually by utilizing various methods and technology to solve the problem in managing oil palm plantation. The aim of this paper is to review the technique in managing oil palm plantation towards a digitalized online 3D application. Various problems and techniques on managing oil palm plantation has been reviewed which involving various technology such as GIS, GPS, DBMS and hyperspectral. It was found that monitoring the characteristic of oil palm plantation is beneficial and important to oil palm planters. The new online 3D application for oil palm plantation management has a potential of assisting oil palm managers in making a decision, visualizing their plantation in online 3D environment, and managing their plantation effectively

On modeling of interviewee motivation mental states for an intelligent coaching agent

This paper is on agent based model of interview motivation to be integrated in a mental constructs model which serves as a basic mechanics for an intelligent virtual agent coaching for job interview. It has been hypothesized that interview motivation combines with self-efficacy and anxiety to define the mental state of a job interviewee. The concepts were modeled based on psychological theories defining human mental state in a time bounded tasking situation like job interview. The proposed model was formalized and simulated to according to its temporal behaviours. The results of the simulation conform to patterns of a number of relations and casual effects on motivation identified in literature. Additionally, the formal model has been automatically verified using Temporal Trace Language (TTL) to find out which stable situations exist. Consequently, this model can serve as a platform for designing an intelligent agent that can understand the metal state of the user during job interview coaching session

Study of noise robustness of First Formant Bandwidth (F1BW) method

The performance of speech recognition application under adverse noisy condition often becomes the topic of researchers regardless of the language used. Applications that use vowel phonemes require high degree of Standard Malay vowel recognition capability.In Malaysia, researches in vowel recognition is still lacking especially in the usage of Malay vowels, independent speaker systems, recognition robustness and algorithm speed and accuracy. This paper presents a noise robustness study on an improved vowel feature extraction method called First Formant Bandwidth (F1BW) on three classifiers of Multinomial Logistic Regression (MLR), K-Nearest Neighbors (k-NN) and Linear Discriminant Analysis (LDA).Results show that LDA performs best in overall vowel classification compared to MLR and KNN in terms of robustness capability

Online 3D oil palm plantation management based on game engine: a conceptual idea

The main commodity export of Malaysia is still oil palm which is known as the backbone of Malaysian agriculture. Various problems and techniques on managing oil palm plantation has been introduces by many researchers and found that monitoring the status of oil palm plantation through online facility is very important. This could be achieved by using game engine technology. This paper discussed the conceptual idea of developing an online 3D oil palm management system based on game engine technology. The game engine can be utilized to simulate the management of an oil palm plantation, as using 3D will allow a lot more information to be conveyed. Game engines allow so much customization as able to construct an online 3D oil palm plantation. This idea would be beneficial to everyone especially future developers who might expand this idea

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