Kawabata Takeshi

Kido Ken\u27iti

Makino Shozo

English

application/pdf学術論文 (Article)59044 bytesjournal articl

Makino, Shozo

Kawabata, Takeshi

Kido, Ken'iti

Tohoku University Repository (TOUR) / 東北大学機関リポジトリ

Recognition of consonant based on thePerceptron model著者 牧野  正三journal orpublication titleIEEE International Conference on ICASSP '83.Acoustics, Speech, and Signal Processingvolume 1983page range 738-741year 1983URL http://hdl.handle.net/10097/46626Recognition of consonant based on the Perceotron modelShozo Makino, Takeshi Kawabato and Ken'iti KidoResearch Center for Applied Information Sciences,Tohoku UniversityAbstractThis paper proposes a new method forthe recognition of consonant based on thePerceptron model. The recognition model iscomposed of the sensory, featureextraction, response and lateralinhibition layers.The recognition scores of 90.4% to98.4% are obtained for unvoicedaffricates, unvoiced plosives, unvoicedand voiced fricatives.1. IntroductionThis paper proposes a new method[l]for phoneme recognition, where thesegmentation and recognition is carriedout simultaneously. The recognition modelis composed of the sensory, featureextraction, response and lateralinhibition layers. The model is similarto the Perceptron model[2] composed ofthe threshold logic unit and the weightedlinear sum.The Perceptron model has two defects;(1) The association layer (featureextraction layer) needs a number of unitsfor extracting features. After the16.7learning, however, most of the units do notcontribute to feature extraction,(2) Physical interpretation for the unitin the association layer selected after thelearning is difficult. With the proposedmodel it is easy to capture the physicalmeaning of the model for the designer.The recognition scores are 93.1%,94.0%, 98,4% and 90.4% for unvoicedaffricates, unvoiced plosives, an unvoicedfricative and an voiced fricative,respectively, for the samples in the 212words uttered by five male and five femalespeakers.2. Recognition modelFig.l shows the structure of modelapplied to recognize the four consonantgroups as shown in Table 1,In the sensory layer, input speech ispassed through 29—channel BPF's.In the feature extraction layer, 6features of the plosive(P), fricative(F),unvoiced(jJ), non—plosive(),non—fricative() and voiced(U) areextracted from the logarithmic spectrum andthe temporal envelope of the logarithmicspeech power, using linear discriminant738 CH1841-6/83/0000-0738 $1.00 © 1983 IEEE ICASSP 83, BOSTONAuthorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:22:07 UTC from IEEE Xplore.  Restrictions apply. function.In the response layer, theconvolutions between the 90 discriminantcoefficients and the 90 vectors composedof the above—mentioned 6 features in 15frames are computed frame by frame, wherethe Perceptron learning is used for makingthe coefficients to optimally discriminatea specified consonant group from theothers. The frame is regarded a consonantif the computed value exceeds thethreshold for the consonant.In the lateral inhibition layer, thefinal output is uniquely determined bylateral inhibition even if some consonantsare recognized in the previous layer.The association unit (featureextraction unit) in the Perceptron modelis randomly connected with the sensoryunit. On the other hand, the featureextraction layer in the present model issystematically connected to extract 6features with physical meaning.Meanwhile, the response layer in the modelis a two dimensional FIR filter in thetime and features axes.3. Sensory layerThe speech is sampled at 24kHz andpassed through 29 single tuned digitalbandpass filters of Q=6, whose centerfrequencies are arranged every 1/6 octavebetween 250 Hz and 6300 Hz. The power ofevery channel is computed for every frameof 10 ms duration and logarithmicallytransformed.4. Feature extraction layerThe logarithmic spectrum of the 29—channel BPF's is defined as vector x.Features of fricative fF(t) and unvoicedfu(t) are as follows.fF(t) wFx(t) (1)= wux(t) (2)The plosive feature is calculated fromthe temporal envelope p(t) of thelogarithmic speech power.f(t) = w(t) (3)where p(t) ( P—Pt ; logarithmic speech power atthe t—th frameThe solution weight vector WF, wand VP for optimally discriminating aspecified phoneme group are computed usingthe discriminant analysis.A number of new ideas are included inthe computation and, as a result, theextracted features yield a clearer physicalinterpretation than thoseFeature extraction Lotera inhibttionlayer layerSensory layer Response layerFig.l Recognition modelTable 1 Phoneme class16.7/c/,/ki/ unvoiced affricates/p/,/t/,/k/ unvoiced plosives/s/ unvoiced fricative/5/ voiced fricativeICASSP 83, BOSTON 739Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:22:07 UTC from IEEE Xplore.  Restrictions apply. obtained with the conventionaldiscriminant analysis. Three complementaryfeatures fp(t), f(t) and f(t) are alsocomputed in the same way.5. Response layerEach phoneme is recognized by theresponse unit independent of the otherunits. Input features f('(t) passthrough a FIR filter as followsM N'r(t) a(1)f(1)(t÷j) (4)i j=—Na(-) ; Filter coefficients forthe i—th fearuref')(t) ; The i—th feature atthe t-th frame.The unit produces output indicatingthe existance of the specified phoneme ifr(t) exceeds the threshold.Filter coefficient a1' is computedusing the Perceptron learning (fixed—increment error correction trainingprocedure [31Using speech samples labeled byvisual inspection, training is carriedout. Hereafter this filter is calleddiscriminant filter.6. Lateral inhibition layerBecause each unit of response layeris constructed independently of eachother, output of the response layer isoften overlapped. The lateral inhibitionlayer determines a unique final outputfrom the multi—output in the previouslayer. Final output is determined usingthe priority relation between the phoneme16.7groups, which is computed using thelearning method the authors havedeveloped [4]7. ExperimentsThe trainings for 4 phoneme andphoneme groups as shown in Table 1 werecarried out for the speech samples in the212 words uttered by 5 male and 5 femalespeakers.Fig.2 shows the coefficeints of thediscriminant filter for the unvoicedaffriates. Unvoiced affricates arecharacterized by the plosive feature (p+)and the succeeding fricative feature(F+).Fig.3 shows the examples of temporalfeature patterns and the output of thediscriminant filters for unvoicedaffricates and unvoiced plosives, where thespecified phonemes are detected correctly.Table 2 shows the detection score anderror scores for the training and testsamples, where Set I and Set II include thetraining and test samples, respectively.The test samples are the 212 words utteredby 5 male and 5 female speakers differentfrom those for the training.8. ConclusionA new method for the recognition ofconsonant is presented and theeffectiveness of the method is shown withthe experimental results. The newrecognition model overcomes the defects inthe Perceptron model. The model is shownto have a clear physical meaning and isexpected to be useful for the phoneme740 ICASSP 83, BOSTONAuthorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:22:07 UTC from IEEE Xplore.  Restrictions apply. recognition in the speech recognitionsystem for unspecified speakers.Reference[1] Kawabata,T., Makino,S. and Kido,K.:!420 \/A40 ,60V 80 (frame)rflvjfl.,I--i\ J c\,60 \J80 (frame)/ s a ku t a C U /:f:_.._\ 20 40 60 A 80 (frame)ICASSP 83, BOSTON 741ULJ-;"Consonant recognition using thePerceptron learning",(in Japanese), Paperof TG on speech, ASJ., S82—24(June,1982)[2] Rosenblatt,F.: "Principle ofNeurodynamics", Spartan Books(196l)[3] Nilsson,N.J.:"Learning Machines.",McGraw—Hill, New York(1965)[41 Kawabata,T., Makino,S. and Kido,K.:"Organization of lateral inhibition.", (inJapanese) , Paper of autumn meeting ofASJ., pp l75—176(Oct.,1982)PFig.2 The examples of the temporalfeature patterns and the outputsof the discriminant filters.Table 2 The detection score and errorscoresFUOUTIN/c//ki/ /p//t//k//s/ /z/ Numberofsamples/c/,/ki/ 93.1% 1.6% 1.6% 2.8% 320/p,t,k/ 3.2 94.0 0.0 0.5 793/s/ 0.0 0.0 98.4 0.1 494/z/ 1.9 0.0 0.5 90.4 209Vowel 0.02 0.3 1.1 1.6 5718Others 0.3 1.9 1.6 3.3 2962Set I'NOUTIN/c//ki/ /p//t//k//s/ /z/ Numberofsamples/c/,/ki/ 90.6% 4.7% 1.6% 2.2% 318/p,t,k/ 3.7 92.7 0.1 0.3 816/s/ 0.2 0.2 94.8 4.2 498/z/ 2.9 0.0 2.4 89.1 210Vowel 0.03 0.05 0.5 0.4 5786others 0.5 2.6 0.6 2.2 3015FUIc! /k/ I j\f\j \ç/'Fig.3 The examples of the temporalfeature patterns and the outputsof the discriminant filters.Set II16.7Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:22:07 UTC from IEEE Xplore.  Restrictions apply. 

Recognition of consonant based on the Perceptron model

Institutional Repositories DataBase (IRDB)

https://tohoku.repo.nii.ac.jp/?action=repository_action_common_download&item_id=26299&item_no=1&attribute_id=18&file_no=1

Recognition of consonant based on the Perceptron model

Abstract

Similar works

Full text

Available Versions

Tohoku University Repository (TOUR) / 東北大学機関リポジトリ

Institutional Repositories DataBase (IRDB)