ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM FOR SPEECH RECOGNITION THROUGH SUBTRACTIVE CLUSTERING

Transcription

1 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November 014 ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM FOR SPEECH RECOGNITION THROUGH SUBTRACTIVE CLUSTERING Samya Slarb 1, Bendahmane Abderrahmane ; and Abdelkader Benyettou 3 Faculty of mathematcal and computer scence, department of Computer Scence, Unversty of Scences and Technology Oran USTO-MB, Algera. ABSTRACT Fuzzy modelng requre two man steps whch are structure dentfcaton and parameter optmzaton, the frst one determnes the numbers of membershp functons and fuzzy f-then rules, whle the second dentfes a feasble set of parameters under the gven structure. However, the ncrease of nput dmenson, rule numbers wll have an exponental growth and there wll cause problem of rule dsaster. In ths paper, we have appled adaptve network fuzzy nference system ANFIS for phonemes recognton. The approprate learnng algorthm s performed on TIMIT speech database supervsed type, a pre-processng of the acoustc sgnal and extractng the coeffcents MFCCs parameters relevant to the recognton system. Frst learnng of the network structure by subtractve clusterng, n order to defne an optmal structure and obtan small number of rules, then learnng of parameters network by hybrd learnng whch combne the gradent decent and least square estmaton LSE to fnd a feasble set of antecedents and consequents parameters. The results obtaned show the effectveness of the method n terms of recognton rate and number of fuzzy rules generated. KEYWORDS ANFIS, subtractve clusterng, Phoneme, recognton. 1. INTRODUCTION Automatc Speech Recognton has acheved substantal success n the past few decades but more studes are needed because none of the current methods are fast and precse enough to be comparable wth human recognton abltes [1,]. Many algorthms and schemes based on dfferent mathematcal paradgms have been proposed n an attempt to mprove recognton rates [3,4,5,6]. Neuro-fuzzy modelng s a combnaton of fuzzy logc and neural network that takes advantage of both approaches, process mprecse or vague data by fuzzy logc [7] and at the same tme by ntroducng learnng through neural network. Several archtectures have been proposed dependng on the type of rule they nclude Mamdan or Sugeno [8] [9] one of the most nfluental fuzzy models has been proposed by Robert Jang n [10] called Adaptve Network Based Fuzzy Inference System ANFIS. The rule base of ths model contans the fuzzy f-then rule of Takag and Sugeno's type n whch consequent parts are lnear functons of nputs nstead of fuzzy sets, reducng the number of requred fuzzy rules. The dentfcaton of fuzzy model conssts of two major phases: structure dentfcaton and parameter optmzaton. The frst phase s the determnaton of number of fuzzy f-then rules and DOI : /jaa

2 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November 014 membershp functons of the premse fuzzy sets whle the second phase s the tunng of the parameter values of the fuzzy model [11]. However, there wll be two problems f we drectly use the tradtonal Takag Sugeno model for the speech recognton system. The frst one s the network reasonng wll fal f the nput dmenson s too large. The second problem s that wth the ncrease of nput dmenson, rule numbers wll have an exponental growth and cause rule dsaster. Thus, determnaton of an approprate structure becomes an mportant ssue, then clusterng technques are appled to solve ths problem. [1] [13] [14]. Ths paper present a neural fuzzy system ANFIS for speech recognton. The approprate learnng algorthm s performed on TIMIT speech database supervsed type, a pre-processng of the acoustc sgnal and extractng the coeffcents MFCCs parameters relevant to the recognton system. Subtractve clusterng s appled n order to defne an optmal structure and obtan small number of rules, then learnng of parameters network by hybrd learnng whch combne the gradent decent and least square estmaton LSE. The paper s organzed as follows: the secton revews the lterature research work, n secton 3 detals the structure and Prncpe of learnng of ANFIS, whle secton 4 descrbes subtractve clusterng; expermental results and dscusson were detaled n secton 5. Secton 6 concludes the paper.. RELATED WORK The ANFIS has the advantage of good applcablty because t can be nterpreted as local lnearzaton modelng, and even as conventonal lnear technques for both state estmaton and state control whch are drectly applcable. Ths adaptve network has good ablty and performance n system dentfcaton, predcton and control has been appled n many dfferent systems. Snce there are not many research works used ANFIS n speech recognton, t s necessary to carry on the exploraton and the thorough research on t. ANFIS was used for Speaker verfcaton [15] usng combnatonal features of MFCC; Lnear Predcton Coeffcents LPC and the frst fve formants; Recognton of dscrete words [16], Speech emoton verfcaton [17] based on MFCC for real tme applcaton. In [18] ANFIS was performed to reduce nose and enhance speech, also for the recognton of solated dgts wth speaker-ndependent [19]. Speaker, language and word recognton was completed by ANFIS [0], furthermore for caller behavor classfcaton [1]. All of these works had used clusterng technques to determne the structure of ANFIS. Another work: an automated gender classfcaton s performed by ANFIS []. 3. ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM ANFIS 3.1. The Concept and Structure ANFIS proposed by Jang n 1993 mult-layered neural network whch connectons are not weghted or all weghts equal 1[10], s alternate method whch combnes the advantages of two ntellgent approaches neural network and fuzzy logc to allow good reasonng n quantty and qualty. A network obtaned has an excellent ablty of tranng by means of neural networks and lngustc nterpretaton of varables va fuzzy logc. The both of them encode the nformaton n parallel and dstrbute archtecture n a numercal framework. ANFIS mplement a frst order Sugeno style fuzzy system; t apples the rule of TSK Takag Sugeno and Kang form n ts archtecture. 44

3 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November 014 Rule: f x s A1 and y s B1 then f ( x) px + qy + r Where x and y are the nputs, A and B are the fuzzy sets, f are the output, p, q and r are the desgn parameters that determned durng the tranng process. ANFIS s composed of two parts s the frst part s the antecedent and the second part s the concluson, whch are connected to each other by rules n network form. Fve layers are used to construct ths network. Each layer contans several node sts structure shows n fgure 1. layer1: executes a fuzzfcaton process whch denotes membershp functons (MFs) to each nput. In ths paper we choose Gaussan functons as membershp functons: 1 O ( x c ) µ exp A σ (1) layer: executes the fuzzy AND of antecedents part of the fuzzy rules o w µ µ A B ( x ) ( ), 1,,3, 4 1 x () layer3: normalzes the MFs o 3 w w w 4 j 1 j, 1,,3, 4 (3) layer4: executes the concluson part of fuzzy rules O 4 ( x + α x + α ), 1,,3,4. w y w α (4) 1 3 layer5: computes the output of fuzzy system by summng up the outputs of the fourth layer whch s the defuzzfcaton process. O overll _ output w y w w y (5) Fgure1. ANFIS archtecture. Crcles n ANFIS represent fxed nodes that predefned operators to ther nputs and no other parameters but the nput partcpate n ther calculatons. Whle square are the representatve for adaptve nodes that affected by nternal parameters. 45

4 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November Learnng Algorthm The parameters to be tuned n an ANFIS are the membershp functon parameters of each nput, the consequents parameters also number of rules. nbr rule n _ m (6) Where n s number of nputs and m number of membershp functons by nput and they generate all the possble rules. Two steps of tranng are necessary whch are: Structure learnng whch allows to determnate the approprate structure of network, that s, the best parttonng of the nput space (number of membershp functons for each nput, number of rules). And parametrc learnng carred out to adjust the membershp functons and consequents parameters. In most systems the structure s fxed a pror by experts. In our work we combne both of learnng sequentally The subsequent to the development of ANFIS approach, a number of methods have been proposed for learnng rules and for obtanng an optmal set of rules. For example, Mascol et al [3] have proposed to merge Mn-Max and ANFIS model to obtan neuro-fuzzy network and determne optmal set of fuzzy rules. Jang and Mzutan [4] have presented applcaton of Lavenberg-Marquardt method, whch s essentally a nonlnear least-squares technque, for learnng n ANFIS network. In another paper, Jang [5] has presented a scheme for nput selecton and [6] used Kohonen s map to tranng. Four methods have been proposed by Jang [11] for update the parameters of ANFIS: All parameters are update by only gradent decent. In frst the consequents parameters are obtaned by applcaton of least square estmaton LSE only once and then the gradent decent update all parameters. Sequental LSE that s usng extended Kalman flter to update all parameters. Hybrd learnng: whch combne the gradent decent and LSE to fnd a feasble set of antecedents and consequents parameters. The most common tranng algorthm s the hybrd learnng. ths algorthm s carred out n two steps: forward pass and backward pass, Once all the parameters are ntalzed, n forward pass, functonal sgnals go forward tll fourth layer and the consequents parameters are dentfed by LSE. After dentfyng consequents parameters, the functonal sgnals keep gong forward untl the error measure s calculated. In the backward pass, the error rates propagate backward and the premse parameters are updated by gradent decent. The functon to be mnmzed s Root Mean Square Error (RMSE): RMSE N 1 N 1 ( ) d o (7) Where d s the desred output and O s the ANFIS output for the th sample from tranng data and N s the number of tranng samples. 46

5 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November SUBTRACTIVE CLUSTERING Clusterng s the classfcaton of objects nto dfferent groups, or more precsely, the parttonng of a data set nto subsets (clusters), so that the data n each subset shares some common features, often proxmty accordng to some defned dstance measure. From the machne learnng perspectve, clusterng can be vewed as unsupervsed learnng of concepts [7][8]. as: 1. Compute the ntal potental value for each data pont x s defned P n j 1 exp x x ( ra ) j (8). Where ra s a postve constant representng a neghborhood radus. Therefore, a pont would have a heght potental value f t has more neghbor ponts close to tself. 3. the pont wth the hghest potental value s selected as le frst cluster center: Frst cluster center x c1 s chosen as the pont havng the largest densty value D c1 4. P ( 1) * ( 1 ( ) max P ( x ) (9) 5. The potental value of each data pont x s reduced as follows: P x x c 1 P Pc 1 exp ( rb ) Where ( rb β ra ) s a postve constant represent the radus of the neghborhood for whch sgnfcant potental reducton wll occur. β s a parameter called as squash factor, whch s multply by radus values to determne the neghborng clusters wthn whch the exstence of other cluster centers are dscouraged. After revsng the densty functon, the next cluster center s selected as the pont havng the greatest potental value. Ths process s repeated to generate the cluster centers untl maxmum potental value n the current teraton s equal or less than the threshold. 5. STRUCTURE LEARNING ALGORITHM FOR T-S FUZZY NEURAL NETWORK ANFIS offers three approaches to dentfy cluster namely grd parttonng, subtractve clusterng and fuzzy c-means clusterng. Grd parttonng approach s useful f the number of features s no more than 6 or 7. If the number of features s too hgh then ths method wll cannot be used as the memory requrement wll be nsuffcent when usng MATLAB. For fuzzy c-means method, the number of clusters for the dataset needs to be specfed. Snce no pror knowledge on the number of clusters s avalable, subtractve clusterng wll be deal. The radus of the clusters wll be used as the bass of the cluster formaton. A range of radus from 0.0 to. has been chosen n order to produce optmum number of clusters for the ntal fuzzy nference system (FIS). The ntal FIS then was fed to the ANFIS for refnng the FIS so that t wll tune the supervsed learnng teratvely wth more detaled rules generated. (10) 47

6 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November 014 Each cluster center represents a fuzzy f-then-rule. The nth column of cth cluster center s assumed to be the mean value (c n ) of the assocated Gaussan membershp functon defned for cth fuzzy set of nth nput varable. Then, the standard devaton (a n ) of the above mentoned Gaussan functons are calculated as below: Therefore the cluster centers and squash factors may be vewed as parameters, whch the number of fuzzy rules of the ntal FIS depend on, before the rule base parameters of the FIS s tuned by ANNs n ANFIS. 6. EXPERIMENTAL AND RESULTS 6.1. TIMIT Database A reduced subset of TIMIT [9] -Phonetc Contnuous Speech Corpus (TIMIT Texas Instruments (TI) and Massachusetts Insttute of Technology (MIT)) used n our work, whch s the abrdged verson of the complete testng set, conssts of 19 utterances, 8 from each of 4 speakers ( males and 1 female from each dalect regon). In general, phonemes can grouped nto categores based on dstnctve features that ndcate a smlarty n artculatory, acoustc and perceptual. The major classes of phonetc TIMIT database are: 6 vowels {/ah/, /aw/, /ax/, /ax-h/, /uh/, /uw/}, 6 frcatves {/dh/, /f/, /sh/, /v/, /z/, /zh/} and 6 plosves {/b/, /d/, /g/, /p/, /q/, /t/}. 6.. Codng Mel-Frequency Cepstral Coeffcents MFCC Before any calculaton, t s necessary to effect some operatons to shape the speech sgnal. The sgnal s frst fltered and then sampled at a gven frequency (16 KHZ). Pre-emphass s carred out to rase the hgh frequences. Then, the sgnal s segmented nto frames, each frame has a fxed number N5ms of speech samples (perod n whch the speech sgnal can be consdered as statonary).treatng small fragments of sgnal leads to flterng problems (edge effects). In order to reduce edge effects we use weghts wndows (Hammng wndow) these are functons that are appled to set of samples n the wndow of the orgnal sgnal. After sgnal shapng a dscrete Fourer transform DFT partcularly fast Fourer transform FFT s appled to pass n the frequency doman and for extractng the spectrum sgnal. Then, the flterng s performed by multplyng the spectrum obtaned by flters (trangular flters). It s possble to employ the output of the flter bank as nput to the recognton system. However, other factors derved from the outputs of a bank of flters are more dscrmnatve, more robust and less correlated. It s from the cepstral coeffcents derved of the flter bank outputs dstrbuted lnearly on the Mel scale, these are the Mel-frequency Cepstral coeffcents MFCC Each wndow provdes 1 MFCC coeffcents and the correspondng resdual energy Results The corpus that we have used n the classfcaton of phonemes conssts of 6 vowels, 6 frcatves and 6 plosves: nstances for learnng and 1055 nstances for test. Applyng subtractve clusterng to determnate the ntal structure of ANFIS, then Hybrd learnng to fnd a feasble set of antecedents and consequents parameters. (11) 48

7 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November 014 We appled ANFIS on the classfcaton of phonemes by varyng the radus of the clusterng between [0.0-.5], results are summarzed n the followng tables: Wth a radus between [0-1.4] we acheved a recognton rate of 100 but the number of generated rules was too bg, so that exceeds 00 rules whch has resulted a large runtme tme and especally for real-tme applcaton, and ths for the three classes of phonemes used. For vowels we have obtaned the best recognton rate of 100 and 6 rules wth a radus of.0 and 1.9, we have reduced the number of rules from 6 to 4 wth a radus of.1 and acheved recognton rate of 83 (table 1). For frcatves wth a radus of 1.4 we have obtaned the best recognton rate that reaches 100 and 5 fuzzy rules, we could reduce the number of rules to 4 wth a recognton rate of (table ). For plosves we reached 100 recognton rate and 5 rules wth a radus of 1.4 and 1.5 beyond 1.5 we had 3 rules but reducton of recognton (table 3). Fgure. Rate recognton dependng of radus Fgure3. Error dependng of radus 49

8 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November 014 Table1. Vowels. Raterecognton error Number-rules Radus tran test tran test tran test Table. Frcatves. Rate-recognton error Number-rules Radus tran test Tran test tran test Radus Table 3. Plosves. Rate-recognton error Number-rules tran test tran test tran test A large number of rules are generated by reducng the radus of clusterng so most of data are correctly classfed but t takes sgnfcant tme. Contrarwse, by ncreasng the radus of clusterng we had very few rules so many alternatves are not consdered by the fuzzy rules, then a lot of data are not correctly classfed. We could see that there was a compromse between the recognton rate and the number of rules generated, wth a radus between [ ] we were able to acheve perfecton on the recognton rate and number of generated rules whch engender a very reasonable computaton tme. 50

9 Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November CONCLUSION AND FUTURE WORK In ths paper, we have appled adaptve network fuzzy nference system for phonemes recognton. Frst learnng of the network structure by subtractve clusterng, n order to defne an optmal structure and obtan small number of rules, then learnng of parameters network by hybrd learnng whch combne the gradent decent and least square estmaton LSE to fnd a feasble set of antecedents and consequents parameters. The approprate learnng algorthm s performed on TIMIT speech database supervsed type, a pre-processng of the acoustc sgnal and extractng the coeffcents MFCCs parameters relevant to the recognton system. Fnally, hybrd learnng combnes the gradent decent and least square estmaton LSE of parameters network. The results obtaned show the effectveness of the method n terms of recognton rate and number of fuzzy rules generated. For future work, consder ths adaptve network for whole dataset TIMIT, and mprovng learnng algorthm by tunng parameters of ANFIS by means of evolutonary algorthms. REFERENCES [1] R. Reddy (001), Spoken Language Processng: A gude to Theory, Algorthm, And System Development, Prentce-Hall, New Jersey. [] X. He, L. Deng (008), Dscrmnatve Learnng for Speech Recognton: Theory and practce, Morgan & Claypool. [3] Cole, Ron, Hrschman, Lynette, Atlas, Les, Beckman, Mary, Bermann, Alan, Bush, Marca, et al (1995) The challenge of spoken language systems: Research drectons for the nnetes. IEEE Transactons on Speech and Audo Processng, vol 3(ssue 1). [4] Scofeld, M. C (1991) Neural networks and speech processng. Amsterdam: Kluwer Academc. [5] Yallop, C. C. (1990) An ntroducton to phonetcs and phonology. Cambrdge, MA: Blackwell. [6] Carla Lopes and Fernando Perdgão. chapter 14 (011) Phone Recognton on the TIMIT Database. Book Speech Technologes Edted by Ivo Ipsc, ISBN , 43 pages, Publsher InTech. [7] George Bojadzev,Mara Bojadzev (1995) Advances n fuzzy systems applcatons and theory, vol 5 book Fuzzy Sets, Fuzzy Logc, Applcatons, World Scentfc. [8] Ajth Abraham (001) Neuro Fuzzy Systems: State-of-the-Art Modelng, Technques n Proceedngs of 6th Internatonal Work-Conference on Artfcal and Natural Neural Networks, IWANN 001 Granada, Span, June 13 15, Sprnger Verlag Germany, vol 084, pp [9] B. Kosko (1991) Neural Networks and Fuzzy Systems A Dynamc Systems Approach, Prentce Hall. [10] J.S.R. Jang (1993) ANFIS: Adaptve Network Based Fuzzy Inference Systems, IEEE Trans, Syst. Man Cybernet. vol 3, No 3, pp [11] J.S.R. Jang, C.T.Sun and E.Mzutan (1997) Neuro-fuuzy and soft computng: a computatonal approach to learnng and machne ntellgence, London: prentce-hall nternatonal, USA. [1] Agus Pryono, Muhammad Rdwan, Ahmad Jas Alas, Rza Atq O. K. Rahmat, Azm Hassan & Mohd. Alauddn Mohd. Al (005) Generaton of fuzzy rules wth subtractve clusterng. Jurnal Teknolog, 43(D). Unverst Teknolog Malaysa, pp [13] Chuen-Tsa Sun (1994) Rule-Base Structure Identfcaton n an Adaptve-Network-Based Fuzzy Inference System. IEEE Transacton on Fuzzy Systems, vol., no. 1. pp [14] Chng-Chang Wong and Cha-Chong Chen (1999) A Hybrd Clusterng and Gradent Descent Approach for Fuzzy Modelng. IEEE Transactons on systems, man, and cybernetcs part b: cybernetcs, vol. 9, no. 6. pp [15] V.Srhar, R.Karthk and R.Antha and S.D.Suganth (010) Speaker verfcaton usng combnatonal features and adaptve neuro-fuzzy nference systems, IIMT 10 December 8-30, Allahabad, UP, Inda, pp

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