Total views : 118

Issues and Challenges of Voice Recognition in Pervasive Environment

Affiliations

  • Maharishi Markandeshwar University, Mullana - 133207, Haryana, India

Abstract


Objectives: To provide detailed study of all the algorithms and analyse them in a way to conclude that an integration approach describes the best combination for voice recognition in terms of recognition rate. Methods: Voice processing is a process in which words of a speaker are recognized by the information of the waves. There are number of algorithms used for voice recognition named as Perceptual Linear Prediction, Linear Predictive Code, Mel Frequency Cepstral Coefficient, Dynamic Time Warping etc. Findings: Graph is used to depict the recognition rate of all the voice recognition techniques with different types of classifiers named as HMM (Hidden Markov Model), DTW ( Dynamic Time Wrapping), VQ (Vector Quantization), SVM (Support Vector Machine) etc. which clearly describes that the hybrid approach may provide better results as compared to individual methods. Performance and recognition rate is not so good by using individual techniques because it does not provide better recognition rate while taking into consideration the security of an individual living alone at home. After the comparative analysis, it is concluded that there is a need to develop a better combined approach which may provide better recognition rate as compared to individual methods. This paper will help the researchers to know the basic difference between the explained feature extraction techniques. Application: Main application we are using is Voice Recognition in order to provide security to an individual living alone at home.

Keywords

Feature Extraction, LPC, MFCC, PLP, Voice Recognition

Full Text:

 |  (PDF views: 66)

References


  • Saxena A, AmitSinha, Chakrawati S, Charu S. Speech Recognition using Mat lab. International Journal of Advances in Computer Science and Cloud Computing. 2013 Nov; 1(2):1–5.
  • Vibha. MFCC and its Applications in Speaker Recognition. International Journal on Emerging Technologies. 2010 Feb; 1(1):19–22.
  • Kurzekar PK, Deshmukh RR, Vishal B, Shrishrimal PP. A Comparitive study of Feature Extraction Techniques for Speech Recognition System. International Journal of Innovative Research, Engineering and Technology. 2014 Dec; 3(12):1–11.
  • Prithvi P, Kishore T. Comparative Analysis of MFCC LFCC RASTA-LPP. International Journal of Scientific Engineering and Research. 2016 May; 4(5):1–4.
  • Patil RP, Mulla MR. A Review Design and Implementation of Image Acquisition and Voice Based Security System. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2015 Mar; 4(3):1–6.
  • Dave N. Extraction Methods LPC and MFCC in Speech Recognition. International Journal for Advance Research in Engineering and Technology. 2013 Jul; 1(6):1–5.
  • Sinha S, Jain A. Spectral and Prosodic features based Speech Pattern Classification. International Journal Applied Pattern Recognition. 2015; 2(1):96–109. Crossref
  • Swamy S, Ramakrishnan KV. An Efficient Speech Recognition System .Computer Science and Engineering an International Journal. 2013; 3(4):1–7. Crossref
  • Kumar K, Jain A. A Hindi Speech Recognition System for Connected Words using HTK. International Journal Computational Systems Engineering. 2012; 1(1):1–8. Crossref
  • Franke T, Tong C, Ashe MC. The Secrets of Highly Active Older Adults. Journal of Aging Studies. 2013; 27(4):398– 409. Crossref PMid:24300060
  • Kumar J, Prabhakar OP, Sahu NK. Comparative Analysis of Different Feature Extraction and Classifier Techniques for Speaker Identification systems a Review. International Journal of Innovative Research in Computer and Communication Engineering. 2014; 2(1):2760–9.
  • Kavya BM, Chakrasali SV. Performance Analysis of MFCC and LPC techniques in Kannada Phoneme Recognition. International Journal of Advances in Electrical Power System and Information Technology. 2015; 1(2):21–5.
  • Singh V, Kumar V, Tripathy N. A Comparative Study of Feature Extraction Techniques for Language Identification. International Journal of Engineering Research and General Science. 2004; 2(3):286–91.
  • Honig F, Stemmer G, Hecker C, Brugnara F. Revising perceptual linear prediction. Interspeech. 2005; 2997–3000.
  • Abdalla MI, Abokar MH, Gaafar ST. DWT and MFCCs Based Feature Extraction Methods for Isolated Word Recognition. International Journal of Computer Applications. 2013; 69(20):21–6. Crossref
  • Madan A, Gupta D. Speech Feature Extraction and Classification A Comparative Review. International Journal of Computer Applications. 2014; 90(9):20–5. Crossref
  • Kepuska VZ, Elharati HA. Robust Speech Recognition System using Conventional and Hybrid Features of MFCC LPC, PLP, RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions. Journal of Computer and Communications. 2015; 3(6):1–9. Crossref
  • Gamit MR, Dhameliya K. Isolated Words Recognition Using MFCC, LPC and Neural Network. International Journal of Research in Engineering and Technology. 2015; 4(6):146–9. Crossref

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.