Earticle

초록

영어

Malay speech recognition is becoming popular among Malaysian researchers. In Malay-sia, 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 capa-bility of an improved vowel feature extraction method called Spectrum Delta (SpD). The fea-tures 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 classi-fiers on a single framed analysis which makes this result comparable to those analysed with multi-framed approach.

목차

Abstract
 1. Introduction
 2. Researches in Malay Speech Recognition
 3. Vowel Recognition Process
  3.1. Determining the Frame Size and Duration
  3.2. SpD Feature Extraction Method
  3.3. Vowel Classification Techniques
 4. Feature Analysis
  4.1. Vowel Classification
  4.2. Noise Robust Analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Shahrul Azmi M.Y. [ College of Arts and Sciences, Universiti Utara Malaysia, Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605