International Journal of Electronic Engineering Research ISSN 0975 - 6450 Volume 2 Number 3 (2010) pp. 377–381 © Research India Publications http://www.ripublication.com/ijeer.htm

Independent Speaker Recognition for Native English Vowels

1G.N. Kodandaramaiah, 2M.N. Giriprasad and 3M. Mukunda Rao

1HOD, Department of Electronics and Communications Engineering, Madanapalli Institute of Technology, Madanapalli, India

2Principal, Jawaharlal Nehru Technological University, Pulivendula, India 3Honorary Research Professor, Biomedical Sciences

Sri Ramachandra Medical College & Research Institute, Chennai, India E-mail: kodandramaiah@yahoo.com

Abstract

This paper presents the standard method for vocal tract shape estimation has been the basis for many successful automatic speech recognition (ASR) systems. Analytic results presented demonstrate that estimation of vocal tract shape, based on reflection co-efficients obtained from LPC analysis of speech, is satisfactory and is related to the place of articulation of the vowels. Here we describe a “standard” approach for classification of vowels based on formants, which are meaningfully distinguishable frequency components of human speech. These formant frequencies depend upon the shape and dimensions of the vocal tract, Vocal tract shape is characterized by a set of formant frequencies, and different sounds are produced by varying the shape of the vocal tract, leading to the property of spoken speech. It has been implemented in many of speech related applications such as, speech/speaker recognition. This work uses Euclidean distance measure, is applied in order to measure the similarity or the dissimilarity between two spoken words, which take place after quantizing a spoken word into its code book. Keywords: Speech, Vocal tract, Formants, Euclidean distance.

Introduction The Fig 1.1 shows the block diagram of Independent Speaker Recognition for vowels. Let S(n) be the test sample of a vowel. Then parameters i.e. formants F1 and F2 are extracted.

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The extracted formants are compared with the threshold of reference formants. Euclidean distance measure is applied in order to measure the similarity or the dissimilarity between two spoken words, which take place after quantizing a spoken word into its code book. The matching of an unknown vowel is performed by measuring the Euclidean distance between the features vector (formants) of the unknown vowel to the reference model (codebook) of the known vowel formants F1, F2 in the database. The goal is to find the codebook that has the minimum distance measurement in order to identify the unknown vowel (Franti et al., 1997). For example, in the testing or identification session, the Euclidean distance between the features vector formants F1, F2 and codebook for each spoken vowel is calculated and the vowel with the smallest average minimum distance is picked as shown in the Eq. (1.1). Note that xi is the i th input features vector (formants F1, F2), yi is the i th features vector in the codebook (Reference Model) and distance d is the distance between xi and yi.

d(x,y)=√[ ] 1.1

where D=2, xi is the ith input features vector (formants F1, F2), yi is the ith feature vector in the code book (Reference Model) and d is the distance between xi and yi.s,Wi = weight associated with ith feature vector, recognition score. Decision Rule The weights ‘w’ are important to use if the information contained in the underlining features is not proportional to the feature variances. In this case of vowel recognition based on formants F1 and F2, they do not uniformly contribute to vowel recognition. Based on study, relative weights-F1 =2; F2 =1 are given but normalized such that the sum of the weights is 1.0. We refer to classification based on this distance as Maximum Likelihood Regression, since this is based on Gaussian assumptions used to obtain the parameters in the classifier. To provide verification that the vowels displayed are producing accurate results, the MLR has calculated the distance of average features for the given vowels.If the feature distance is within the threshold criteria Di (F1, F2), then equation 1.1 becomes Di (f) < α√m 1.2 where m is number of features i.e. F1 and F2, α is arbitrary scale factor used for performance tuning. Then, the vector xi is identified as the vector yi, otherwise not. If it is too small the MLR rejects many correct vowel samples. If it is too large the output of category vowels will not be rejected. In our work the threshold α=x has given optimum results.

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Figure 1.1: Block diagram of vowel recognition.

Result of Vowel Ecognition of Male and Female Speakers Male Speakers The table 3.1 gives the result for male vowel recognition based on MLR method. Vowel /a/ has achieved perfect classification compared to other vowels. The detection rate for vowel /u/ and /e/ is better than vowel /o/ and /i/ for all tested samples. Vowel /e/ and vowel /i/ tend to mis-classify with each other due to the variations of utterances from different inter-speakers. The Fig 3.1 shows vowel ‘X’ versus % vowel recognition for 50 male samples, where ‘X’ is the actual vowel. For vowel /a/, /a/ in /a/ is 46; /a/ in /e/ is 0; /a/ in /i/ is 4;/a/ in /o/ is 0;/a/ in /u/ is 0. Hence the percentage correctness of recognition of vowel /a/ is = ( /a/ in /a/ )* 100÷(/a/ in all the vowels) = 46 * 100÷(46+0+4+0+0) = 46*100/50 = 92 %.

Figure 3.1: Vowel Vs % vowel recognition for male speaker.

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Table 3.1 Shows the percentage recognition for vowel of male speakers.

vowels Predicted Actual /a/ /e/ /i/ /o/ /u/ % correct

/a/ 46 0 4 0 0 92% /e/ 2 44 0 4 0 89% /i/ 6 0 40 0 4 80% /o/ 0 3 0 44 3 88% /u/ 3 1 1 0 45 90%

Female Speakers The table 3.2 gives the result for female vowel recognition based on MLR method. Vowel /o/ has achieved perfect classification compared to other vowels. The detection rate for vowel /u/ and /e/ is better than vowel /a/ and /i/ for all tested samples. Vowel /a/ and vowel /i/ tend to mis-classify with each other due to the variations of utterances from different inter-speakers. The Fig 3.2 shows the percentage of recogniton of vowel for 40 female samples. For vowel /o/, /o/ in /a/ is 0; /o/ in /e/ is 0; /o/ in /i/ is 0;/o/ in /o/ is 39;/o/ in /u/ is 1. Hence the Percentage correctness of recognition of vowel /o/ is = ( /o/ in /o/ )* 100÷(/o/ in all the vowels) = 39 * 100÷(0+0+0+39+1) = 39*100/40 = 98 %.

Figure 3.2: Vowel Vs % vowel recognition for female speaker.

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Table 3.2 Shows percentage recognition of vowel for female speakers

Vowel /a/ /e/ /i/ /o/ /u/ %correct /a/ 34 4 0 0 2 85% /e/ 0 37 0 3 0 92% /i/ 0 4 34 0 2 86% /o/ 0 0 0 39 1 98% /u/ 3 0 0 0 37 94%

Conclussion It was an attempt presents to the standard method for vocal tract shape estimation has been the basis for many successful automatic speech recognition (ASR) systems. Here we describe a “standard” approach for classification of vowels based on formants. We achieved 80 to 95 percentage of speaker recognition using Euclidean distance measure. Acknowledgements We would like to thanks the Management, Principal of Madanapalli Institute of Technology and Science, Madanapalli, A.P., for their Cooperation and Encouragement References [1] L.R.Rabiner and R.W.Schafer, Digital processing of Speech signals, Droling

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[2] Thomas F. Quatieri, Discrete time speech signal processing principles and practice,2002, pp 56-59.

[3] P. Ladefoged, R. Harshman, L. Goldstein, and L. Rice, “Generating vocal tract shapes from formant frequencies,” J. Acoust. Soc. Am., vol. 64, no. 4, , 1978, pp. 1027–1035.

[4] Mayukh Bhaowal & Kunal Chawla Isolated word Recognition for English Language using LPC, Vq and HMM,pp.2-4.

[5] G.E Peterson and H.L Barney,” control methods used in a study of the vowels ” J.Acoustic.Soc.Amer., Volume 24,PP.175-184

[6] P.Rose,Long-and short-term within-speaker differences in the formants of Australian hello, j.Int. Phonetic. Assoc. 29(1) (1999) 1-31.

[7] AhmedAli Safiullah Bhatti, dr.Munammad Sleem Miam. formants based Analysis for speech recognition, IEEE 2006.

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