Contains reports on two research projects.National Science Foundatio

Engel, A. I.

Halle, Morris

Hughes, G. W.

Jacobsen, H. J.

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X. SPEECH ANALYSISProf. M. Halle G. W. Hughes H. J. JacobsenA. I. Engel F. PozaA. VOWEL IDENTIFIERMost vowel identifiers constructed in the past were designed on the principle of"pattern matching"; that is, for each vowel a pattern of some set of measurable param-eters, such as a particular energy density spectrum, frequency location of the formants,axis crossing density, and so on, was chosen to represent the average or "ideal" set forthat vowel. These patterns were stored in the apparatus, and the results of measure-ments on an unknown input were compared with the patterns in order to obtain the bestmatch, which was interpreted as the uttered vowel. Implicit in these schemes is theassumption that with each utterance of a vowel, the speaker is trying to hit a compli-cated target pattern of such measurable parameters. This assumption has never beenproven, nor is it logically the only possible one.An alternative model, the distinctive feature model, stresses the fact that identifi-cation is possible so long as the speaker differentiates each vowel from all other vowelsin his language in some consistent manner. Since with optimal coding it is possible todistinguish 2 n different vowels by means of n binary features, a distinctive featuremodel is evidently very economical. It also obviates the necessity of assuming thestringent requirements of accuracy in producing the vowels that are demanded in the"pattern matching" hypothesis. As long as the relevant features are properly produced,identification will be possible, regardless of other measurable features of the pattern.The description of these relevant features in terms of electrical measurements may bequite complex, but the feature "pattern" associated with each vowel is simply stated inbinary terms of presence or absence of the pertinent features. Thus this approacheliminates the storage of complex parameter patterns, but may entail an involvedmeasurement procedure to extract information in terms of distinctive features.Since all properties of the acoustic stimulus except those serving to distinguish dif-ferent words in the language are ignored as carrying more information about the speaker,the linguistic and extra-linguistic context, and so forth, than about the vowel that isspoken, identification should be relatively independent of the speaker provided his dialectpossesses the selected features.Table X-l(a) gives a summary of the results of a theoretical analysis of a set ofAmerican English vowels on the basis of the four pertinent distinctive features. If theindicated binary decision is made for each feature, every vowel shown will be uniquelyspecified. Note that in four cases specification can be made on the basis of only threeSupported in part by National Science Foundation.109Table X-1. Distinctive Feature Analysis of Vowels.(a)FeatureTense-LaxGrave -AcuteCompact -NoncompactDiffuse -Nondiffuse/i/ /1 /el /T L T L TA A A A AN N N N CD D N N R// /a/ /A/ /0/ /Z/ /u/L T L T L TA G G G G GC C C N N NR R R N N DR = Redundant (feature not necessary to distinguish this phoneme)(b)above 1400below 1400above 700below 700150-400400-700cpscpscpscpscpscpsAcuteGrave feature 2Compactfeature 3Noncompact feature 3Noncompact + DiffuseNoncompact + Nondiffuse/U/LGND2nd2nd1st1st1st1stformantformantformantformantformantformant(X. SPEECH ANALYSIS)features. Thus, if the acoustical correlates of these features can be found, and theappropriate measurements instrumented, an "unknown" vowel input can be classifiedmechanically.It has been shown that frequency position of the first three formants is sufficientto specify completely the spectral envelope of a vowel sound. Therefore, this param-eter was chosen as the one in terms of which the features indicated in Table X-1(a) wereformulated. A preliminary investigation of approximately 60 detailed energy densityspectra of the vowels of Table X-l (spoken by several speakers in word context) indi-cated that the simple formulation given in Table X-l(b) was sufficient as far as features2, 3, and 4 were concerned, since the feature compact never occurs with diffuse-nondiffuse. To simplify the construction of the present vowel identifier, feature 1(tense-lax) was ignored, thus allowing only a six-category classification, hereafterdesignated as /i/, /e/, /a- /, /a/, /o/, and /u/ (the tense member of the pair coalescedwith the lax one as the result of omitting feature 1).DESCRIPTION OF IDENTIFIERA device was built for the purpose of exploring the possibilities of the model outlinedin the preceding section rather than of producing a finished piece of equipment. Thispreliminary vowel identifier locates the first two vowel formants relative to the sixfrequency regions given in Table X-l(b) and carries out the logical procedure requiredby the feature analysis given in Table X-l. The device consists of three main parts:a filter set, a pulse-position modulator, and a relay selector circuit.The output of the microphone (into which the vowel that is to be identified is spoken)is amplified and fed into a set of six fixed bandpass filters. These filters are of thecascaded, m-derived type with very sharp cutoff characteristics (120 db/octave skirtslope). The frequency bands of the filters that enable location of formant position[consistent with Table X-l(b)] are given in Table X-2. The small discrepancies infrequency band limits can be ascribed to the finite skirt slopes and to the fact that theclosest available filters were used. Table X-2 also shows which two of the given setof filters pass maximum energy for each of the six vowel categories.The outputs of the six filters are fed into a pulse-position modulator which locatesthe bands of highest energy and second-highest energy. The details of this section ofthe vowel identifier are described in the appendix. In essence, this component trans-forms the voltage-level information emanating from the filters into the time domain.Thus the filter channel with the highest output produces a pulse that comes first in timeduring a fixed sampling period; the channel with the next highest output produces a pulsethat follows the first with a delay proportional to the difference in level between the twochannels, and so forth, for each of the six channels.The relay selector circuit accepts these pulses spaced in time, and on the basis of111(X. SPEECH ANALYSIS)Table X-2. Filter Frequencies.FrequencyRange165-365 cps365-645645-800800-1100990-13701370-2400o(o) u(U)2,5 1,5Vowel categoryHighest energy output channelsTable X-3. Logical Connections.Filter of highest energy123456 (neveroccurs)Relays so connected that:2, 3, 41,3,41,2,41,2, 365cannotcannotcannotcannotcannotcannotsecondsecondsecondsecondsecondsecondThe first two filters that have been selected as having highestenergy output subject to the constraints above uniquely determinevowel indication.112FilterChannel1234561,6 2, 63,6or4,63,5or4,5i(I) e(E) ~(- ) a(A)(X. SPEECH ANALYSIS)which came first and second, causes an indicator to flash under one of the six vowelclassifications in accordance with the combinations indicated in Table X-2. Simpleinterconnection of thyratrons and relays would have accomplished this selection butfor the fact that formant peaks are often broad enough to cause adjacent channels tofire first and second in order. In any actual vowel input, adjacent formants are notthis close (except for channels 4 and 5); therefore certain disenabling connections weremade. These are given in Table X-3. Thus, for example, an actual sequence of pulsesmight be 2 - 1 - 5 - 6, which would be identified as 2 - 5 or as belonging to the vowelclass /o/.EVALUATION OF PERFORMANCEThis preliminary version of a vowel identifier based on distinctive feature principleswas tested by having many speakers speak vowels into the machine and by noting itsoperation. The errors of the machine can be ascribed, in most cases, to limitationson obtaining fine formant frequency discrimination when as in the present case only sixfilter channels are used. Dialectal variations of the vowels /ae / and /o/ accounted fora great many additional errors. Although, theoretically, the system is independent ofthe quality and pitch of the voice, for extremely high-pitched female voices errors dooccur frequently. It is interesting to note that whispered vowels are identified reason-ably well. In general, most errors are caused by locating the first formant in a bandadjacent to the correct one. It is felt that the use of more channels would help makefirst formant frequency location more precise. A series of tests is being planned thatwill present short recorded segments of vowels for identification by both a group oflisteners and the machine, in order to obtain better performance evaluation.Further tests on the response of the vowel identifier were made with a vowel reso-nance synthesizer that was built by Gunnar Fant. This machine (called OVE by Fant)allows manual control of the circuits that simulate the first three resonances (formants)of the vocal tract. They are connected in cascade and driven by a buzz source of variablepitch which simulates the larynx. The output is very close to real speech waveforms ofvowel sounds, but unlike natural speech it can be precisely controlled by the experi-menter. The frequency location of formants l(F1) and 2(F2) is controlled by movinga pointer over a calibrated plot of Fl versus FZ. Additional controls adjust F3 and thefundamental frequency.We present the results of feeding the output of OVE into the vowel identifier inFigs. X-1 and X-2, where lines are plotted on the FI-F2 plane representing boundariesbetween the six vowel categories into which the identifier is forced to place all inputs.F3 was held fixed at 3000 cps. Figure X-1 shows the category boundaries that areobtained by using the filter cutoff frequencies given in Table X-2 and a fundamentalpitch frequency of 120 cps from OVE. Figure X-2 shows these boundaries under the113900,800300Fig. X-1. A plot on the Fl-FZ plane of the boundariesbetween phoneme categories with the vowelidentifier excited by OVE; fundamental pitchfrequency, 120 cps.1000-900,800250Fig. X-2. A plot on the Fl-F2 plane of the boundariesbetween phoneme categories with the vowelidentifier excited by OVE; fundamental pitchfrequency, 200 cps.(X. SPEECH ANALYSIS)same conditions except that the pitch was raised to approximately 200 cps. The widerspacing of the harmonics causes some distortion of the boundary lines and the appear-ance of spurious regions, since the energy in a given frequency band is more sensitiveto variations in harmonic frequencies when the fundamental frequency is high (widelyspaced harmonics) than when many harmonics are present in the band (low fundamental).APPENDIX. DESCRIPTION OF THE PPM CIRCUITThis part of the identifier (see Fig. X-3) consists of six separate, identical circuitsthat transform the output of each of the filters into a single pulse. The time between agiven pulse output and a zero reference time is proportional to the rectified and smoothedvoltage of the corresponding filter output. This voltage-to-time transformation isaccomplished by detecting a coincidence between a downward linear sweep and therectified filter output voltage. The three essential parts of the circuit are: (1) theamplifier, rectifier, and smoothing RC integrator; (2) a phantastron sweep generator;and (3) a multiar coincidence detector with associated multivibrator and pulse-shapingcircuits.Briefly, the operating cycle of one of the six identical channels is as follows: Ini-tially, the sweep voltage at one input to the coincidence circuit is stationary at +10 volts,and the rectified-integrated voltage from the filter at the other input to the coincidencecircuit is zero. As a vowel is spoken into the microphone the rectified-integratedvoltage rises until the channel whose bandpass filter is passing maximum energy reaches+10 volts. A coincidence pulse which starts the sweep downward is generated. Thesweep reaches bottom at slightly below zero volt in about 30 msec, while the RC timeconstant of the integrator is 300 msec. As the sweep passes the integrated voltages onthe other channel coincidence circuits, pulses whose time order represents the filtersin order from highest to lowest output are produced. These pulses, as well as theinitial one which defines time zero, are fed to the relay selector chassis which carriesout the logical procedure indicated in Tables X-2 and X-3 for vowel identification. Whenthe sweep reaches bottom, a pulse is generated, triggering a relay circuit that resetsall integrator outputs to zero and extinguishes the thyratrons in the selector circuit.Thus the initial conditions are restored and the cycle is repeated until the vowel inputto the microphone ceases.This discussion is based, in large part, on two theses that were submitted in partialfulfillment of the requirements for the S. B. degree in the Department of ElectricalEngineering, M.I. T., 1956: "A device for locating peaks and intensities of the vowelspectra," by Harry James Jacobsen, and "Automatic vowel recognizer operating ondistinctive feature principles," by Alan I. Engel.G. W. Hughes, H. J. Jacobsen, A. I. Engel, M. Halle115CHANNEL 4PULSE OUTTO RELAYSELECTOR CIRCUITFIRST PULSESTARTS SWEEPRELAY SELECTORCIRCUITFig. X-3. Block diagram showing circuit connections in the vowel identifier.The top row represents only one of the six identical channels.(X. SPEECH ANALYSIS)B. STOP STUDIESThe two major cues for stop consonants are the stop burst and the transitions of theformants in the adjacent vowels. Detailed energy density spectra of the isolated stopbursts were prepared, and criteria for identification of the spectra were developed andtested. The reliability of these criteria was compared with the reliability of humanlisteners in identifying the isolated bursts. It was found that human listeners, espe-cially if trained, can identify stop bursts correctly in a large number of cases. Theobjective criteria mentioned above, although not quite as reliable as the best listeners,provided correct identification in the large majority of instances.Transitions were studied by means of sonagrams. Great difficulties were experi-enced in stating simple criteria, like those proposed for synthetic speech. Transitionsin the formants of adjacent vowels were studied by means of sonagrams of isolated wordsrecorded by several speakers. Unlike synthetic speech, natural speech did not permitthe formulation of simple criteria. The transitions, although not uncorrelated with thenature of the following stop, failed to provide sufficient information for a definitiveidentification. This conclusion was apparently supported by the evidence obtained inperceptual tests with vowel and stop sequences in which the stop bursts had been gatedout so that the stop cue was entirely in the vowel transitions. The identifications ofthese stimuli were about as reliable as would have been predicted from an examinationof the sonagrams.Details of this investigation are included in a paper by Halle, Hughes, and Radley,"Acoustic properties of stop consonants," accepted for publication by the Journal ofthe Acoustical Society of America, January 1957.M. Halle, G. W. Hughes117

Speech Analysis

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Speech Analysis

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