Kido Ken\u27iti

Makino Shozo

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Makino, Shozo

Kido, Ken'iti

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A speaker independent word recognition system based on phoneme recognition for a large size (212 words) vocabulary

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A speaker independent word recognition systembased on phoneme recognition for a large size(212 words) vocabulary著者 牧野  正三journal orpublication titleIEEE International Conference on ICASSP '84.Acoustics, Speech, and Signal Processingvolume 1984number 9page range 17.8.1-17.8.4year 1984URL http://hdl.handle.net/10097/46630A SPEAKER INDEPENDENT WORD RECOGNITION SYSTEM BASED ON PHONEME RECOGNITIONFOR A LARGE SIZE ( 212 WORDS ) VOCABULARYShozo Makino and Ken' iti KidoResearch Center for Applied Information Sciences,Tohoku University,Sendai 980, Japan.ABSTRACTThis paper describes the speaker—independentspoken word recognition system for a large sizevocabulary. Speech is analyzed by the filter bank,from whose logarithmic spectrum the 11 featuresare extracted every 10 ms. Using the features thespeech is first segmented and the primary phonemerecognition is carried out for every segment usingthe Bayes decision method. After correcting errorsin segmentation and phoneme recognition, thesecondary recognition of part of the consonants iscarried out and the phonemic sequence isdetermined. The word dictionary item havingmaximum likelihood to the sequence is chosen asthe recognition output. The 75.9% score for thephoneme recognition and the 92.4% score for theword recognition are obtained for the trainingsamples in the 212 words uttered by 10 male and 10female speakers. For the same words uttered by 30male and 20 female speakers different from theabove speakers, the 88.1% word recognition scoreis obtained.1. INTRODUCTIONA number of speaker independent systems17)has been presented so far, All systems, except forthose developed by Chiba et al.4 and Rabiner etai,6'7, were based on the phoneme recognition,where recognized speech samples were uttered onlyby adult male speakers. The systems developed byChiba et al. and Rabiner et al. require a largeamount of data uttered by a number of speakers, inthe case where the change in vocabulary isrequired.On the contrary, the new spoken wordrecognition system described in this paper canrecognize a large size vocabulary uttered byunspecified adult male and female speakers at ahigh performance. The system is composed of thephoneme recognition part and the word recognitionpart, where segmentation and phoneme recognitionare carried out. The vocabulary can easily bechanged from the key—board since the worddictionary is represented in phonemic symbols.2. SYSTEM OUTLINEFigure 1 shows a schematic diagram of thespoken word recognition system based on thephoneme recognition.Speech is passed through the Z9channeldigital bandpass filters ( single tuned circuitsof 0=6 ), whose center frequencies are arrangedevery 1/6 octave between 250Hz and 6300 Hz. Thepower of every channel is computed for every frameof 10 ma duration and logarithmically transformed.The 9 features are extracted from the logarithmicspectrum of each 10 ms—frame, The primarysegmentation is carried out using the first ordertime—derivative of the features. Using the 11features composed of the above 9 and other twofeatures extracted from the temporal pattern oflogarithmic power, the phoneme of the detectedsegment is recognized by applying the Bayesdecision method, on the assumption of multi-dimensional normal distribution. Errors in theprimary segmentation and phoneme recognition arecorrected by the error correction rules.CSpoken work).Jl[Frequency analysis]____ ItPreprocessing[ [Calculation ofofspectrun[ [logarithmic spectrumL..ummat ionExtraction of]____________________8 features [ iiraction of voii]________________and unvoiced plosives]Calculation ofparameter forphoneme boundarydetection[Primary segmentati[1l ___ -17.8.1[Primary phoneme recognitionjError correction i Jorsegmentation and[.uz —[correctionphoneme recogniti] [rulesSecondary phoneii]recognitionPhonemic sequeJCalculation of likelihood Confusion matrix[between every item of theword dictionary and the Word dictionaryjphonemic sequence represented inj___________________ phonemic symb[CRecognition ouçpFig 1 Schematic diagram of spoken wordrecognition systemCH1945-5/84/0000-0186 $1.00 © 1984 IEEEAuthorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 08:16:57 UTC from IEEE Xplore.  Restrictions apply. 0 ( c_N,•,cO,••,eN)= ( Vj_N vj Vj+N ), N=7O : Solution weight vectorSequence of LS for the j—th frameLogarithmic spectrum summation LS forthe j—th framewhere the solution weight vector c iscomputed using the discriminant analysisfor optimally discriminating betweenplosive and the others.Meanwhile, the secondary phoneme recognition is local minimum of the temporal pattern is alsocarried out using the time spectrum pattern methodpreviously proposed8 for nasals, unvoiced andassumed to be the typical frame of the segment.There are a number of segment insertions which arevoiced plosives and unvoiced fricative. The mostly eliminated using the higher levellikelihood between the phonemic sequence and each information.item of the word dictionary is calculated, wherethe item with maximum likelihood is chosen as therecognition output.3.3 Recognition of phonemesThe two parameters for unvoiced and voicedplosives are computed using the discriininant3. SEGMENTATION AND PHONEME RECOGNITION filters from the temporal envelope of thelogarithmic spectrum summation LS. The outputof the discriininant filter for the j—th frame isdefined as follows:3.1 Feature extraction using discriminantanaiysis)The eight features are computed for everyframe from the logarithmic spectrum using thediscriminant analysis.—Ny = Cjv.+i ()1=—NFor the feature expressed by the weighted sumof p parameters the condition for the optimal= cv3discrimination of a specified phoneme group fromthe other is given by the maximization of Fisherratio e:O = atBal at Wa — max (1)W : Covariance matrix within classB : Covariance matrix between classesW is rewritten using the KL expansionW = FA Ft (2)A1A2='. The eight features, the logarithmic spectrum0 Am summation LS and the two parameters for plosivesare used for phoneme recognition. Figure 2 showsF ={ 1'2" 'i''m the solution weight vectors for part of thefeatures. The shapes show the good correspondenceEigen value of Wto the physical images. The feature for voicedplosive is useful for discriminating betweenf' : Eigen vector of W consonants and vowels. Figure 3 shows thetemporal pattern of the 11 features and thewhere rank of I is m less thanSolution weight vector a is given by Eq.(3).detection parameter of the phoneme boundary forthe word /rakuda/ uttered by a adult male, whereeach of the horizontal lines indicate thea = FAlFtd (3) threshold for the feature. All features arecorrectly extracted.d : The distance vector between mean vectors. a/ouie3.2 Phoneme boundary detectionThe following power parameter is used for thedetection of phoneme boundary.(1) Logarithmic spectrum summation LS2.-•LS= S1 (4)i=1 ch—e.S : Logarithmic output of the i—th channel voiced plosiveThe eight features and the logarithmicspectrum summation LS show the large changes atthe phoneme boundaries. For that reason they arepassed through the first order time—derivativefilters. The solution weight vector for phonemeboundary detection is computed using thediscriminant analysis. The weighted summation ofthe absolute values of the time—derivative filteroutput is computed frame by frame. The frame withthe local maximum of the temporal pattern of theweighted summation is assumed to be the segment Fig. 2 Solution weight vectors for part ofboundary. On the other hand, the frame with the the featuresfricative/the othersunvoiced plosiveframe —17.8.2Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 08:16:57 UTC from IEEE Xplore.  Restrictions apply. Using aforementioned 11 parameters, thephoneme recognition is carried out for the typicalframe by applying the Bayes decision method,assuming multi—dimensional normal distribution.Standard pattern of each phoneme is made using thesamples manually extracted by visual inspection.4. ERROR CORRECTION IN SEGMENTATION ANDRECOGNITIONIn order to correct the errors insegmentation and phoneme recognition, thefollowing information is used:(1) phoneme contexts,(2) the contexts where the omissions occur, and(3) phoneme duration.Based on these information, the fivecorrection rules are defined as follows:(1) When the succeeding two or more segments arerecognized as the same phoneme, those segmentsare merged into one segment unless the totalduration exceed the upper limit of theduration.(2) The segment with duration shorter than thelower limit is absorbed by the surroundingsegment, if one of the top—three recognitionresults for the shorter segment is identicalwith one of the surrounding segment.(3) When the vocalic segment appears after thenon—sound segment, the plosive segment isinserted between the two segments.(4) When the succeeding two segments arerecognized as unvoiced phoneme and each ofthese segments satisfies the condition ofduration, the devocalized vocalic segment isinserted between the segments.(5) When the succeeding two segments arerecognized as vowels, phoneme recognition iscarried out for the frames around the boundarybetween these segments; a new phoneme isinserted if the result satisfies the certainconditions.Using the rules above, most of the errors insegmentation and recognition of phonemes arecorrected.5. SECONDARY PHONEME RECOGNITION USING TIMESPECTRUM PATTERN METIIOD8Secondary phoneme recognition is carried Outusing the time spectrum pattern( TSP ) method fordiscriminating among the phonemes with the samemanner of articulation. The TSP method assumes thetime spectrum pattern( 29channels by 5frames ) ofa phoneme as the multi—dimensional vector derivedfrom the normal distribution. Recognition ofconsonants using the TSP method is carried out forthe frames around the epoch point, where the epochpoint for nasals is the final stationary framefrom the nasal consonant to the following voweland that for plosives is the burst frame. Theepoch point is detected by the change in thelogarithmic power of the specified frequencyrange. The standard patterns are made usingsamples around the epoch point manually extractedby visual inspection. The dimension of the timespectrum pattern is reduced from 145 to 24 usingthe Karhunen—Loeve expansion. Secondaryrecognition is carried out using the TSP method ifthe result of recognition in the previous stagebelongs to one of the four phoneme groups:Im,n,/, Ip,t,kI, Jb,d,g/ and/s.d.905 27 IRUMNOFeatureslogarithmic spectrumsummationvoiced/unvoicednasal/the othersfricative/the othersa/ouieo/auieu/aoiei/aouee/aouiunvoiced plosivevoiced plosivedetection parameterof phoneme boundaryPhonemelabel=2 —.—-=-----.----—-—-\_—-_________:__---—- —-..._______—-- .-----__-J------.->---v------—----> TimeFig. 3 The temporal pattern of the 11 features and the detectionparameter of the phoneme boundary for the word /rakuda/17.8.3Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 08:16:57 UTC from IEEE Xplore.  Restrictions apply. Thus determined is the phonemic sequence withthe top—three results sent to the word recognitionstsge.6. WORD RECOGNITION BASED ON THE PHONEMIC SEQUENCEIn the word recognition part, a number ofsub—items are generated referring to the confusionmatrices of phoneme recognition for initial—, mid—and final positions of words. This is followed bythe computation of similarity between the phonemicsequence with the top—three recognition resultsand each sub—item of of the word dictionary item.Probabilities of insertions, omissions andsubstitutions of phonemes included in confusionmatrices are taken into account for computation.The rank of phoneme recognition is also taken intoaccount. The confusion matrices are madeaccording to the results of experiments. Thecomputation time for similarity includinggeneration of sub—items is considerably reduced bythe utilization of dynamic programming. Thedictionary item having the maximum similarity tothe sequence is chosen as the recognition output.7. RECOGNITION EXPERI MENTSPhoneme recognition experiments are carriedout for 212 words uttered by 10 male and 10 femalespeakers. The solution weight vectors for thefeatures and the standard patterns for thephonemes are made using the same sample wordgroup. By using the TSP method, the scores forthe unvoiced and voiced plosives are increased by18% and 20%, respectively. The scores of phonemeomission and insertion are 6.4% and 14.7%,respectively, whereas the phoneme recognitionscore is 75.9%.Table 1 shows the word recognition scores,The word recognition score of 92.4% is obtainedfor the samples from which the confusion matricesfor the word recognition are made. For the samewords uttered by 30 male and 20 female speakersdifferent from the above—mentioned speakers, theword recognition score of 88.1% is obtained. Thescores for female speakers are nearly the same asthose for male speakers.8. CONCLUSIONThis paper describes a spoken wordrecognition system for a large vocabulary recentlydeveloped. The score of phoneme and wordrecognition are 75.9% and 92.4%, respectively.The system recognizes the spoken words based onthe phoneme recognition word recognition iscarried out using the likelihood between thephonemic sequence transformed from the inputspeech and the word dictionary item written inphonemic symbols. The vocabulary to be recognizedcan easily be altered by changing the dictionaryitem from the key—board. The large vocabulary sizeis also one of the features of the system.Table 1 Word recognition scores17.8.4REFERENCES(1) Itabashi S., S. Makino and K. Kido, "Discrete—Word Recognition Utilizing a Word Dictionaryand Phonological Rules" , IEEE Trans., AU—2l,3, pp239—248( June 1973(2) Sakai T. and S. Nakagawa, "A ClassificationMethod of Spoken Words in Continuous Speecbfor Many Speakers " ( in Japanese ),Information Processings Soc. Japan, 17, 7,pp65O—657( July 1976(3) Kido K., T, Matsuoka, J. Miwa and S. Makino,"Spoken Word Recognition System for UnlimitedAdult Male Speakers" , Trans. IECE Japan, E61,8, pp593—598( Aug. 1978(4) Chiba S., M, Watari and T. Watanabe, "A SpokenWord Recognition System for Unlimited Speakers"( in Japanese ), presented at the meeting ofIECE Japan, 219( Aug. 1977(5) Miwa J., Y. Niitau, S. Makino and K. Kido,"Spoken Word Recognition Systems using GrossFeatures of Speech Spectrum and these DynamicProperties" ( in Japanese ), J. Acoust. Soc.Japan, 34, 3, ppl86—193( Mar. 1978(6) Rabiner L. R. " On Creating ReferenceTemplates for Speaker Independent Recognitionof Isolated Words" , IEEE Trans., ASSP—26, 1.ppS4—42( Feb. 1978(7) Rabiner L. R. and J. G. Wilpon, " Speaker—Independent Isolated Word Recognition for aModerate Size(54 Word ) vocabulary " , IEEETrans., A55P—27, 6, pp583—587(Dec. 1979(8) Ide K., Makino S. and Kido K., " Recognitionof unvoiced plosives using Time SpectrumPattern " ( in Japanese ), J. Acouat. Soc.Japan, 39, 5, pp32l—329( Aug. 1983(9) Kawabata T,, Makino S. and Kido K,, "FeatureExtraction of Phoneme Based on theDiacriminant Analysis " ( in Japanese ), Trans.of the IECE of Japan, J65—A,l2, ppl2lS—l285(Dec. 1982Training 10 males 93.7% Average 4240set 10 females 91.3% 92.4% samplesTeat 30 males 87.0% Average 10600set 20 females 89.6% 88.1% samplesAuthorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 08:16:57 UTC from IEEE Xplore.  Restrictions apply. 

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A speaker independent word recognition system based on phoneme recognition for a large size (212 words) vocabulary

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