i-manager Publications

Feature Extraction in Speech Recognition using Linear Predictive Coding: An Overview

D. Suja Darling*, J. Hinduja**

* Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

** Department of Electronics and Communication Engineering, Udaya School of Engineering, Ammandivilai, Tamil Nadu, India.

Periodicity:July - December'2022
DOI : https://doi.org/10.26634/jdp.10.2.19289

Abstract

Over the past years, advancements in speech processing have mostly been driven by DSP approaches. The speech interface was designed to convert speech input into a parametric form for further processing (Speech-to-Text) and the resulting text output to speech synthesis (Text-to-Speech). Feature extraction is done by changing the speech waveform into a parametric representation at a relatively low data rate so that it can be processed and analyzed later. There are numerous feature extraction techniques available. This paper presents the overview of Linear Predictive Coding (LPC).

Keywords

Digital Signal Processing, Speech Recognition, Linear Predictive Coding, Feature Extraction.

How to Cite this Article?

Darling, D. S., and Hinduja, J. (2022). Feature Extraction in Speech Recognition using Linear Predictive Coding: An Overview. i-manager’s Journal on Digital Signal Processing, 10(2), 16-21. https://doi.org/10.26634/jdp.10.2.19289

References

[1]. Agrawal, S., Shruti, A. K., & Krishna, C. R. (2010). Prosodic feature based text dependent speaker recognition using machine learning algorithms. International Journal of Engineering Science and Technology, 2(10), 5150-5157.

[2]. Ainsworth, W. (1988). Speech Recognition by Machine. Peter Peregrinus.

[3]. Analog Devices. (n.d.). Beginner's Guide to Digital Signal Processing (DSP). Retrieved from https://www.analog.com/en/design-center/landing-pages/001/beginners-guide-to-dsp.html

[4]. Bou-Ghazale, S. E., & Hansen, J. H. (2000). A comparative study of traditional and newly proposed features for recognition of speech under stress. IEEE Transactions on Speech and Audio Processing, 8(4), 429-442. https://doi.org/10.1109/89.848224

[5]. Bradbury, J. (2000). Linear Predictive Coding. McGraw Hill.

[6]. Diniz, P. S., Da Silva, E. A., & Netto, S. L. (2002). Digital Signal Processing: System Analysis and Design. Cambridge University Press.

[7]. Kumar, R., Ranjan, R., Singh, S. K., Kala, R., Shukla, A., & Tiwari, R. (2009). Multilingual speaker recognition using neural network. Proceedings of the Frontiers of Research on Speech and Music, FRSM, 1-8.

[8]. Narang, S., & Gupta, M. D. (2015). Speech feature extraction techniques: a review. International Journal of Computer Science and Mobile Computing, 4(3), 107-114.

[9]. Nehe, N. S., & Holambe, R. S. (2012). DWT and LPC based feature extraction methods for isolated word recognition. EURASIP Journal on Audio, Speech, and Music Processing, 2012(1), 1-7. https://doi.org/10.1186/1687-4722-2012-7

[10]. Rabiner, L., & Juang, B. (2003). Fundamentals of Speech Recognition. Pearson Education.

[11]. Rabiner, L., & Juang, B. H. (1993). Fundamentals of Speech Recognition. Prentice-Hall, Inc.

[12]. Shrawankar, U., & Mahajan, A. (2013). Speech: a challenge to digital signal processing technology for human-to-computer interaction. Proceedings National Conference on Recent Trends in Electronics & Information Technology (RTEIT), 206-212. https://doi.org/10.48550/arXiv.1305.1925

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Feature Extraction in Speech Recognition using Linear Predictive Coding: An Overview

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: