KIDO Ken\u27iti

MAKINO Shozo

MORIAI Satoshi

English

application/pdf学術論文 (Article)133819 bytesjournal articl

MORIAI, Satoshi

MAKINO, Shozo

KIDO, Ken'iti

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

Phoneme recognition in contiunous speech usingphoneme discriminant filters著者 牧野  正三journal orpublication titleIEEE International Conference on ICASSP '86.Acoustics, Speech, and Signal Processingvolume 1986number 11page range 2251-2254year 1986URL http://hdl.handle.net/10097/46628PHONEME  RECOGNITION IN CONTINUOUS SPEECH USING  PHONEME  DISCRIMINANT FILTERS Satoshi MORIAL, Shozo MAKINO and Ken' i t i  KID0 Kesearch Center for Applied Information Sciences Tohoku University Sendai, Japan ABSTE2ACT We describe  in  this paper  a new  phoneme recognition  system  using phoneme discriminant f i l ters  for  speaker  independent  continuous  speech. The Segmentation of speech and the  c lass i f icat ion of phoneme are  carried  out  simultaneously by the phoneme discr iminant  f i l ters .  Each of them detects the  candidate of the  specified phoneme in  input speech  independent of the  other phonemes. The final  recognition  result  is  uniquely  determined from the  outputs of t he   f i l t e r s  through  the unification process. The recognition  scores  are 88.1% for  the t ra ining set  and 76.0% for  the  tes t  set uttered by the other 5 male speakers. INTRODUCTION This  paper  proposes  a new  phoneme recognition system  using phoneme discriminant  f i l ters  for speaker  independent  continuous  speech  recognition. This system is based on the piecewise linear clas- s i f i e r  and the  rror  correction  learning method, and is composed of  the  parallel phoneme discrimi- nant f i l t e r s  and the unif ier .  In  usual phoneme recognition  systems[1,2],  the segmentation of speech and the  classification of phoneme are   t rea ted   as independent par t ,   the  features used in the segmentation part are obtained by heuris t ic  methods,  and the  classification is carried  out by complicated  rules  based on the specified models for each kind of phonemes.  Conse- quently, it is d i f f i c u l t   t o  improve individual parts of the system for a higher accuracy. To solve  this problem, we introduce  the  system based on an unique recognition model for  a l l  k inds of phonemes i n  which segmentation and c l a s s i f i -  cation  are  carried  out  simultaneously. The  model is s imilar   to  Kawabata's consonant  recognition model[3]. However, the new system  uses  the time spectrum pattern  as  the  feature  vector  for  the discriminant  f i l ters and the  unifier  determines  a single  output  for  each window from the  overlapped outputs. The system i s  trained with the speech samples in the 212 words uttered by 5 male speakers. The recognition  scores  are 88.1% for  the  training set and 76.0% for the test set uttered by the other 5 male speakers. Inhibitors (Gates) Yl , --[delay] ICASSP 86, TOKYO Figure 1 Outline of phoneme recognition system using phoneme discr iminant  f i l ters .  42. 7. 1 CH2243-4/86/0000-2251 $1.00 0 1986 IEEE o u t p u t  2251 Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:51:15 UTC from IEEE Xplore.  Restrictions apply. PHONEME RECOGNITION SYSTEM Sys  tern  out 1 ine Figure 1 shows  an  outline of the  phoneme  recog- nition  system  using  phoneme  discriminant  filters. Speech  is  sampled  at 24 kHz  and  passed  through the 29 channel single tuned digital bandpass filters of Q=6, whose center frequencies are arranged  every 116 octave  between 250 Hz through 6300 Hz. The  logarithmic  power  of  every  channel  is computed  for  every  frame  of 10 ms  duration.  The logarithmic  spectrum  is  normalized  by  subtracting the  mean  value  from  it. In each discriminant filter, the convolutions are  computed  frame  by  frame  between  the  spectra over  several  frames  and  the  weight  vectors,  which are  trained  according  to  the  Perceptron  learning. If  a  computed  value  exceeds  the  threshold  for  a phoneme,  the  frame  is  regarded as a  candidate  of the  phoneme. The  output  of  the  discriminant  filters  is  often overlapped  since  very  filter  is  constructed independently each other. Therefore, the final output  is  uniquely  detennined  through  the  unifica- tion  process.  The  unification  is  carried  out  by comparing  the  value  of  the  weighted  outputs  of  the filters  in  each  time  window,  and  the  phoneme  with the  maximum  value  is  selected as the  representative one  in  the  time  window.  The  time  window  is  shifted frame  by  frame.  The  coefficients  are  decided so as to minimize total recognition error with error correction  learning. Phoneme  discriminant  filter A unit  of  phoneme  discriminant  filter  is  a MA (Moving  Average)  digital  filter as follows, yi(n) = 5 wi(k).x(n+k) + wio, k=K1 where  x(n) : spectrum  at  n-th  frame, wi(n) : weight  vector  for  i-th  phoneme, w . threshold value, and i0 ' [K . ,K, .] : time  window. 11 2 1  The  i-th  discriminant  filter  produces  output  indi- cating  the  existence  of  the  i-th  phoneme  if y. (n) is positive. Each of the filters is comple$ely independently  working.  Since  the  discriminant filter  is  based  on  the  computation  of  convolution and  works  in  parallel,  the  computation  time  is  able to  be  shortened  easily  and  a  long  time  window  can be  used.  This  method  is an effectual  one  to  detect the  phonemes  whose  spectrum  is  changing  with  time. Design  of  a  phoneme  discriminant  filter It  is  difficult  to  design  the  filter  by  statist- ical methods for the case of high dimension. Therefore,  weight  vector  of  each  filter  is  computed using the Perceptron learning (fixed increment error correctgn training  procedure[4]).  The weight  vector w is  modified  by  the  learning  with the  following  algorithm. - w <- w 0 for  each x do if x Ci and ;.x < 0 then AG <- cx else  if  x  Ci  and w.x > 0 then A w  <- -c?i else AW <- o fi W <- W + aG od where, x = (x(n+Kl),x(n+Kl+l), . . . ,  x(n+K2),1) , - t - w (w(K1)  ,w(Kl+l) J . .  . ,w(%) jwo) , t c : learning  constant  (positive),  and Ci : the  i-th  phoneme  category. The  samples  of  the  category Ci for  learning  are  the speech  samples  of  i-th  phoneme in the  tcme  window [K1,K2],  and  the  samples  of  the  category Ci are all the  speech  samples  excluding  the  i-th  phonemes  in the  time  window  of  the  same  length.  To-balance  the number  of  learning  samples  of Ci and Ci, the  sam- ples  in Ci are  used  repeatedly. Phoneme  recognition  based on output  unification[5] A final  output  is  uniquely  determined  through the  unification  process  of  all  outputs  from  the phoneme  discriminant  filters.  Within  a  time  win- dow, overlapped outputs from the filters are weighted with unification coefficients and the phoneme  with  the  maximum  value  is  selected  as  a representative output, where the time window is shifted  frame  by  frame. This  process  is  expressed  by  the  following  equa- t  ion. A i = argmax ai(k)yi(n+k). -K(k<K _ -  Finally, if  i  is  satisfied  the  condition fi Y; (n) > 0 ( 3 )  the  n-th  frame  is  recognized as the  category C: . Unification  coefficient  ai(k)  is  decided so  as to  minimize  the  total  recognition  error  rate  with the rror correction learning (here, fraction correction  rule  is  used)  represented  by  the  follow- ing  algorithm . 42. 7. 2 2252 ICASSP 86, TOKYO Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:51:15 UTC from IEEE Xplore.  Restrictions apply. for  each  sample  do if  a  sample  in Ci is  recognized  as Cj (aiyi < a.y.) then J J  a.y. - a.y. 2 2  c<- J J  1 1  Y; + Yj a. <- ai + cy. a <- a. - cy 1 1 j J j fi od CONCLUSION A new system for the phoneme recognition is presented.  The  scores  are 88.1% for  a  training  set and  76.0%  for  a  test  set. The  new  system  is  based on the  repetitive  learn- ing  of  the  piecewise  linear  classifier,  and  treat all  kinds  of  phonemes  with  an  unique  recognition model.  The  model  comprises  some  elements  to  detect the  candidate  of  each  phoneme  and  a  component  to unify  the  candidates.  The  detection  element  is an MA digital  filter,  and  can  be  processed  in  real time . This  system  is  expected  to  be  useful  for  the speaker  independent  continuous  speech  recognition. EXPERIMENTS The  speech  samples  consist  of 2 individual  sets. Set I is  a  training  set of the  212  words  uttered  by 5 male speakers.  Set  I1  is  a  test  set of the  same 212 words  uttered  by  the  other 5 male  speakers. First  the  phoneme  discriminant  filters,  next  the unifier  were  trained  with  set I. Figure 2 shows the weight vectors of the discriminant  filters.  Where,  the  30th  channel  is  a weight  value  for  mean  power of logarithmic  spectrum at  every  frame.  Figure 2(a) is for  semivowel /w/, /w/  is  characterized  by  the  spectrum  peak  transi- tion from vowel /u/  to /a/. Figure 2(b) is for unvoiced  plosive  It/,  /t/ is characterized  by  the marked  variation  in  mean  power  and  the  spectrum  at plosion. Figure 2(c) is for syllabic nasal /N/, /N/ is  characterized  by  the  continuation  of  a  nasal spectrum. Figure 3 shows  the  error  rates  of  the  filters for  set I and  11. Table 1 shows  the  confusion  matrices  of  phoneme recognition  for  set I and  11.  The  averages  of  the recognition  rates  are  88.1%  (for  set I) and 76.0% (for  set  11). REFERENCES [ l]  C.J.WeinStein, S.S.McCandless, L.F.Mondshein and V.W.Zue, "A system for acoustic phonetic analysis  of  continuous  speech", IEEE Trans.  ASSP- 23-1,  54-67  (1975). [ Z ]  T.Sakai and S.Nakagawa, "A classification method  of  spoken  words  in  continuous  speech  for many  speakers", J. IPS  Jpn.  17,  650-658  (1976)  (in Japanese). [3] S.Makino,  T.Kawabata  and  K.Kido,  "Recognition IEEE ICASSP 83 BOSTON, 738-741 (1983). of  consonant  based on the  Perceptron  model",  Proc. [4] N.J.Nilsson,  Learning  machines , McGraw-Hill, New  York  (1965) . [5]  S.Moriai,  S.Makino  and KKido, "Phoneme recognition sys tern using phoneme discriminant filters",  Trans.  of  the  Committee on Speech  Res. ASJ. S84-71, 557-564 (1984) (in  Japanese). --+frequency bower I -frequency  power -C frequency bower Figure 2 Examples  of  weight  vectors. 42. 7.  3 ICASSP 86, TOKYO 2253 Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:51:15 UTC from IEEE Xplore.  Restrictions apply. ! n Phoneme (a) Set I Phoneme (b) S e t  11 Figure 3 Error rates of the phoneme discr iminant  f i l ters .  Table l ( a >  Recognition  Score [ X ]  (Set I ) .  d 118 4 63 r 247  2.4 1.6 4.9 .8 1.6 1.6 .876.9 2.0 .4 . M   . M  5.2 7. 170 h 147 .7 -7 1.4 .7 2.0 1.4  .7 77.6 .7  8.2 1.4 3.4 1.4 .6 99.4 c 158 2 .5  L9 t 144 k 472 N 269 .8 EfJ.4 2.5 3.4 1.7 5.1 3.2  4.e42.9 4.8 11.1 3.2 30.2 91.2 1.8 1.8 2.9 .6 1.8 s 358 P 3’ 2.7 2.7  2.7  2.7 73.0 2.7 13.5 .7 .7 96.5 2.1 Q 29 .8 -6 .4 .6 1.5 1.7 .4 .4 93.0 .4 .4 .4  .4 .4 92.9  4.1 .7 .7 .4 I 76.9 6.9 3.4 .7 .4  .4 10.3 EfJ.2 y 277 2.9 1.4 4.7 .7 2.5 .4 1.4 .4 Table l ( b )  Recognition  Score [%] (Set 11). k 477 93 .O N 270 Q 40 2.1 1.7 6 .9  79.7 1.9 2.9 . E  .e .2 1.0 .e .2 .6.. .2 .7 46.4110.0 .4 10.0 .4 3.2 2.1 .7 2.9 1.1 4.3 1.1 2.1 3.2 .4 16.8 4.3 1.1 .7 1.1 1.9 2.5 81.5 - Y 2 a  ~ .4 .4 .4 .7 .4 - 2254 42. 7 .  4 ICASSP 86, TOKYO Authorized licensed use limited to: TOHOKU UNIVERSITY. Downloaded on April 08,2010 at 07:51:15 UTC from IEEE Xplore.  Restrictions apply. 

Phoneme recognition in contiunous speech using phoneme discriminant filters

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Phoneme recognition in contiunous speech using phoneme discriminant filters

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