The second-order derivatives of MFCC for improving spoken Arabic digits recognition using Tree distributions approximation model and HMMs

Author

Hammami, Nacereddine ; Bedda, Mouldi ; Nadir, Farah

Author_Institution

Lab. LabGed, Univ. Badji Mokhtar Annaba, Annaba, Algeria

fYear

2012

fDate

26-28 June 2012

Firstpage

1

Lastpage

5

Abstract

Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in many speech and speaker recognition applications. In this paper, we study the effect of the second-order derivatives of MFCC on the recognition of the Spoken Arabic digits. The system was developed using the Hidden Markov Models (HMMs) and Tree distribution approximation model. Experimentally it has been shown that, the second-order derivatives of MFCC parameters compared to the MFCC yield improved rates of 4.60% for CHMM. We were able to reach an overall recognition accuracy of 98.41%, which is satisfactory compared to previous work on spoken Arabic digits speech recognition.

Keywords

approximation theory; cepstral analysis; feature extraction; hidden Markov models; natural language processing; speaker recognition; trees (mathematics); HMM; MFCC; Mel frequency cepstral coefficients; hidden Markov models; second-order derivatives; speaker recognition applications; speech features; speech recognition applications; spoken Arabic digits recognition; tree distributions approximation model; Accuracy; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Speech; Speech recognition; Vectors; Arabic Speech recognition; Cross validation; Hidden Markov Models; Mel Frequency Cepstral Coefficients; Spoken Arabic Digits; Tree distribution approximation;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Information Technology (ICCIT), 2012 International Conference on

Conference_Location

Hammamet

Print_ISBN

978-1-4673-1949-2

Type

conf

DOI

10.1109/ICCITechnol.2012.6285769

Filename

6285769