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sn#051784 filedate 1973-07-03 generic text, type T, neo UTF8
00010 June 26 1973
00020
00030 A Proposal for Speech Understanding Research
00040
00050
00060 It is proposed that the work on speech recognition that is
00070 now under way in the A.I. project at Stanford University be continued
00080 and extended with broadened aims in the field
00090 of speech understanding. This work gives considerable promise both of
00100 solving some of the immediate problems that beset speech
00110 understanding research and of providing a basis for future advances.
00120
00130 It is further proposed that this work be more closely tied to
00140 the ARPA Speech Understanding Research effort than it has been in the
00150 past and that it have as its express aim the study and application to
00160 speech recognition of a machine learning process, that has proved
00170 highly successful in another application and that has already been
00180 tested out to a limited extent in speech recognition. The machine
00190 learning process offers both an automatic training scheme and the
00200 inherent ability of the system to adapt to various speakers and
00210 dialects. Speech recognition via machine learning represents a global
00220 approach to the speech recognition problem and can be incorporated
00230 into a wide class of limited vocabulary systems.
00240
00250 Finally we would propose accepting responsibility for keeping
00260 other ARPA projects supplied with operating versions of the best
00270 current programs that we have developed. The availability of the high
00280 quality front end that the signature table approach provides would
00290 enable designers of the various over-all systems
00300 to test the relative performance of the top-down portions of their
00310 systems without having to make allowances for the deficiencies
00320 of their currently available front ends. Indeed, if the signature table
00330 scheme can be made simple enough to compete on a time basis (and we
00340 believe that it can) then it may replace the other front end
00350 schemes that are currently in favor.
00360
00370 Stanford University is well suited as the site for such work,
00380 having both the facilities for this work and a staff of people with
00390 experience and interest in machine learning, phonetic analysis, and
00400 digital signal processing. The staff at present consists of the
00410 proposed Principal Investigator Arthur l. Samuel, one post-doctoral
00420 staff member Ravindra Thosar, who
00430 had worked on speech recognition and synthesis in India, a
00440 second member Dr. Neil Miller, who has had considerable signal-processing
00450 experience and a few graduate students. It is anticipated that a staff
00460 of not more than 3 full time members with the help of 3 or 4 graduate
00470 students could mount a meaningful program, which should be funded for a
00480 mimimum of two years to ensure continuity of effort.
00490 We would expect to demonstrate the utility of the
00500 Signature Table approach within this time span and to provide a working
00510 system that could be used as the front end for any of the
00520 speech understanding systems that are currently under
00530 development or are being planned.
00550
00560 Ultimately we would
00570 like to have a system capable of understanding speech from an
00580 unlimited domain of discourse and with an unknown speaker. It seems not
00590 unreasonable to expect the system to deal with this situation very
00600 much as people do when they adapt their understanding processes to
00610 the speakers idiosyncrasies during the conversation. The signature table
00620 method gives promise of contributing toward the solution of this
00630 problem as well as being a
00640 possible answer to some of the more immediate problems.
00650
00660 The initial thrust of the proposed work would be toward the
00670 development of adaptive learning techniques, using the signature
00680 table method and some more recent varients and extentions of this
00690 basic procedure. We have already demonstrated the usefulness of this
00700 method for the initial assignment of significant features to the
00710 acoustic signals. One of the next steps will be to extend the method
00720 to include acoustic-phonetic probabilities in the decision process.
00730
00740 Still another aspect to be studied would be the amount of
00750 preprocessing that should be done and the desired balance between
00760 bottom-up and top-down approaches. It is fairly obvious that
00770 decisions of this sort should ideally be made dynamicallly depending
00780 upon the familiarity of the system with the domain of
00790 discourse and with the characteristics of the speaker.
00800 Compromises will undoubtedly have to be made in any immediately
00810 realizable system but we should understand better than we now do the
00820 limitations on the system that such compromises impose.
00830
00840 It may be well at this point to describe the general
00850 philosophy that has been followed in the work that is currently under
00860 way and the results that have been achieved to date. We have been
00870 studying elements of a speech recognition system that is not
00880 dependent upon the use of a limited vocabulary and that can recognize
00890 continuous speech by a number of different speakers.
00900
00910 Such a system should be able to function successfully either
00920 without any previous training for the specific speaker in question or
00930 after a short training session in which the speaker would be asked to
00940 repeat certain phrases designed to train the system on those phonetic
00950 utterances that seemed to depart from the previously learned norm. In
00960 either case it is believed that some automatic or semi-automatic
00970 training system should be employed to acquire the data that is used
00980 for the identification of the phonetic information in the speech. We
00990 believe that this can best be done by employing a modification of the
01000 signature table scheme previously discribed. A brief review of this
01010 earlier form of signature table is given in Appendix 1.
01020
01030 The over-all system is envisioned as one in which the more or
01040 less conventional method is used of separating the input speech into
01050 short time slices for which some sort of frequency analysis,
01060 homomorphic, LPC, or the like, is done. We then interpret this
01070 information in terms of significant features by means of a set of
01080 signature tables. At this point we define longer sections of the
01090 speech called segments which are obtained by grouping together varying
01100 numbers of the original slices on the basis of their similarity. This
01110 then takes the place of other forms of initial segmentation. Having
01120 identified a series of in this way we next use another set of
01130 signature tables to extract information from the sequence of segments
01140 and combine it with a limited amount of syntactic and semantic
01150 information to define a sequence of phonemes.
01160
01170 While it would be possible to extend this bottom up approach
01180 still further, it seems reasonable to break off at this point and
01190 revert to a top down approach from here on. The real difference in
01200 the overall system would then be that the top down analysis would
01210 deal with the outputs from the signature table section as its
01220 primitives rather than with the outputs from the initial measurements
01230 either in the time domain or in the frequency domain. In the case of
01240 inconsistencies the system could either refer to the second choices
01250 retained within the signature tables or if need be could always go
01260 clear back to the input parameters. The decision as to how far to
01270 carry the initial bottom up analysis must depend upon the relative
01280 cost of this analysis both in complexity and processing time and the
01290 certainty with which it can be performed as compared with the costs
01300 associated with the rest of the analysis and the certainty with which
01310 it can be performed, taking due notice of the costs in time of
01320 recovering from false starts.
01330
01340 Signature tables can be used to perform four essential
01350 functions that are required in the automatic recognition of speech.
01360 These functions are: (1) the elimination of superfluous and
01370 redundant information from the acoustic input stream, (2) the
01380 transformation of the remaining information from one coordinate
01390 system to a more phonetically meaningful coordinate system, (3) the
01400 mixing of acoustically derived data with syntactic, semantic and
01410 linguistic information to obtain the desired recognition, and (4) the
01420 introduction of a learning mechanism.
01430
01440 The following three advantages emerge from this method of
01450 training and evaluation.
01460 1) Essentially arbitrary inter-relationships between the
01470 input terms are taken in account by any one table. The only loss of
01480 accuracy is in the quantization.
01490 2) The training is a very simple process of accumulating
01500 counts. The training samples are introduced sequentially, and hence
01510 simultaneous storage of all the samples is not required.
01520 3) The process linearizes the storage requirements in the
01530 parameter space.
01540
01550 The signature tables, as used in speech recognition, must be
01560 particularized to allow for the multi-category nature of the output.
01570 Several forms of tables have been investigated. Details of the current
01580 system are given in Appendix 2. For some early results see
01590 SUR Note 43 "Some Preliminary Experiments in Speech Recognition
01600 Using Signature Tables" by R.B.Thosar and A.L.Samuel.
01620
01630 Work is currently under way on a major refinement of the
01640 signature table approach which adopts a somewhat more rigorous
01650 procedure. Preliminary results with this scheme indicate that a
01660 substantial improvement has been achieved. This effort is described in
01670 a recent report SUR Note 81 on "Estimation of Probability Density Using
01680 Signature Tables for Application to Pattern Recognition, by
01690 R.B.Thosar.
01700
01720 We are currently involved in work on a segmentation
01730 procedure which has already demonstrated its ability to compete with other
01740 proposed segmentation systems, even when used to process speech from
01750 speakers whose utterances were not used during the training
01760 sequence.
00010 FACILITIES
00020
00030 The computer facilities of the Stanford Artificial Intelligence
00040 Laboratory include the following equipment.
00050
00060 Central Processors: Digital Equipment Corporation PDP-10 and PDP-6
00070
00080 Primary Store: 65K words of 1.7 microsecond DEC Core
00090 65K words of 1 microsecond Ampex Core
00100 131K words of 1.6 microsecond Ampex Core
00110
00120 Swapping Store: Librascope disk (5 million words, 22 million
00130 bits/second transfer rate)
00140
00150 File Store: IBM 3330 disc file, 6 spindles (leased)
00160
00170 Peripherals: 4 DECtape drives, 2 mag tape drives, line printer,
00180 Calcomp plotter, Xerox Graphics Printer
00190
00200 Communications
00210 Processor: BBN IMP (Honeywell DDP-516) connected to the
00220 ARPA network.
00230
00240 Terminals: 58 TV displays, 6 III displays, 3 IMLAC displays,
00250 1 ARDS display, 15 Teletype terminals
00260
00270 Special Equipment: Audio input and output systems, hand-eye
00280 equipment (2 TV cameras, 3 arms), remote-
00290 controlled cart
00010 RESEARCH GRANT BUDGET
00020
00030 TWO YEARS BEGINNING OCTOBER 1, 1973
00040
00050
00060 BUDGET CATEGORY YEAR 1 YEAR 2
00070 -----------------------------------------------------------------
00080 I. SALARIES & WAGES:
00090
00100 Samuel, A.,
00110 Senior Research Associate
00120 Principal Investigator, 75% 20,000 20,000
00130
00140 ------,
00150 Research Associate 14,520 14,520
00160
00170 Miller, N.,
00180 Research Associate 13,680 13,680
00190
00200 ------,
00210 Student Research Assistant,
00220 50% academic year, 100% summer 4,914 5,070
00230
00240 ------,
00250 Student Research Assistant,
00260 50% academic year, 100% summer 4,914 5,070
00270
00280 Reserve for Salary Increases
00290 @ 5.5% per year 2,901 5,980
00300 ------- -------
00310
00320 TOTAL SALARIES AND WAGES $60,929 $64,320
00330
00340 II. STAFF BENEFITS:
00350
00360 17.0% 10-1-73 to 8-31-74 9,495
00370 18.3% 9-1-74 to 8-31-75 929 10,790
00380 19.3% 9-1-75 to 9-30-75 1,034
00390 ------- -------
00400 TOTAL STAFF BENEFITS $10,424 $11,824
00410
00420 III. TRAVEL:
00430
00440 Domestic -
00450 Local 150
00460 East Coast 450
00470 ---
00480 $600 $600
00490
00500 IV. EXPENDABLE MATERIALS & SERVICES:
00510
00520 A. Telephone Service 480
00530 B. Office Supplies 600
00540 ---
00550 $1,080 $1,080
00560
00570 V. PUBLICATIONS COST:
00580
00590 2 Papers @ 500 ea. $1,000 $1,000
00600 ------- -------
00610
00620 VI. TOTAL DIRECT COSTS:
00630
00640 (Items I through V) $74,033 $78,824
00650
00660 VII. INDIRECT COSTS:
00670
00680 On Campus - 47% of NTDC $34,796 $37,047
00690
00700 ------- -------
00710 VIII. TOTAL COSTS:
00720
00730 (Items VI + VII) $108,829 $115,871
00740 -------- --------
00010 COGNIZANT PERSONNEL
00020
00030
00040 For contractual matters:
00050
00060 Office of the Research Administrator
00070 Stanford University
00080 Stanford, California 94305
00090
00100 Telephone: (415) 321-2300, ext. 3330
00110
00120 For technical and scientific matters regarding this proposal:
00130
00140 Arthur l. Samuel
00150 Computer Science Department
00160 Stanford University
00170 Stanford, California 94305
00180
00190 Telephone: (415) 321-2300, ext. 4971
00200
00210 For administrative matters, including questions relating
00220 to the budget or property acquisition:
00230
00240 Mr. Lester D. Earnest
00250 Computer Science Department
00260 Stanford University
00270 Stanford, California 94305
00280
00290 Telephone: (415) 321-2300, ext. 4971
00010
00020 Appendix 1
00030
00040 The early form of a signature table
00050
00060 For those not familiar with the use of signature tables as
00070 used by Samuel in programs which played the game of checkers, the
00080 concept is best illustrated (Fig.1) by an arrangement of tables used
00090 in the program. There are 27 input terms. Each term evaluates a
00100 specific aspect of a board situation and it is quantized into a
00110 limited but adequate range of values, 7, 5 and 3, in this case. The
00120 terms are divided into 9 sets with 3 terms each, forming the 9 first
00130 level tables. Outputs from the first level tables are quantized to 5
00140 levels and combined into 3 second level tables and, finally, into one
00150 third-level table whose output represents the figure of merit of the
00160 board in question.
00170
00180 A signature table has an entry for every possible combination
00190 of the input vector. Thus there are 7*5*3 or 105 entries in each of
00200 the first level tables. Training consists of accumulating two counts
00210 for each entry during a training sequence. Count A is incremented
00220 when the current input vector represents a prefered move and count D
00230 is incremented when it is not the prefered move. The output from the
00240 table is computed as a correlation coeficient
00250 C=(A-D)/(A+D).
00260 The figure of merit for a board is simply the
00270 coefficient obtained as the output from the final table.
00010 Appendix 2
00020
00030 Initial Form of Signature Table for Speech Recognition
00040
00050 The signature tables, as used in speech recognition, must be
00060 particularized to allow for the multi-catagory nature of the output.
00070 Several forms of tables have been investigated. The initial form
00080 tested and used for the data presented in the attached paper uses
00090 tables consisting of two parts, a preamble and the table proper. The
00100 preamble contains: (1) space for saving a record of the current and
00110 recent output reports from the table, (2) identifying information as
00120 to the specific type of table, (3) a parameter that identifies the
00130 desired output from the table and that is used in the learning
00140 process, (4) a gating parameter specifying the input, that is to be
00150 used to gate the table, (5) the sign of the gate,
00160 (6) the gating level to be used and (7)
00170 parameters that identify the sources of the normal inputs to the
00180 table.
00190
00200 All inputs are limited in range and specify either the
00210 absolute level of some basic property or more usually the probability
00220 of some property being present. These inputs may be from the original
00230 acoustic input or they may be the outputs of other tables. If from
00240 other tables they may be for the current time step or for earlier
00250 time steps, (subject to practical limits as to the number of time
00260 steps that are saved).
00270
00280 The output, or outputs, from each table are similarly limited
00290 in range and specify, in all cases, a probability that some
00300 particular significant feature, phonette, phoneme, word segment, word
00310 or phrase is present.
00320
00330 We are limiting the range of inputs and outputs to values
00340 specified by 3 bits and the number of entries per table to 64
00350 although this choice of values is a matter to be determined by
00360 experiment. We are also providing for any of the following input
00370 combinations, (1) one input of 6 bits, (2) two inputs of 3 bits each,
00380 (3) three inputs of 2 bits each, and (4) six inputs of 1 bit each.
00390 The uses to which these differint forms are put will be described
00400 later.
00410
00420 The body of each table contains entries corresponding to
00430 every possible combination of the allowed input parameters. Each
00440 entry in the table actually consists of several parts. There are
00450 fields assigned to accumulate counts of the occurrances of incidents
00460 in which the specifying input values coincided with the different
00470 desired outputs from the table as found during previous learning
00480 sessions and there are fields containing the summarized results of
00490 these learning sessions, which are used as outputs from the table.
00500 The outputs from the tables can then express to the allowed accuracy
00510 all possible functions of the input parameters.
00520
00530 Operation in the Training Mode
00540
00550 When operating in the training mode the program is supplied
00560 with a sequence of stored utterances with accompanying phonetic
00570 transcriptions. Each sample of the incoming speech signal is
00580 analysed (Fourier transforms or inverse filter equivalent) to obtain
00590 the necessary input parmeters for the lowest level tables in the
00600 signature table hierarchy. At the same time reference is made to a
00610 table of phonetic "hints" which prescribe the desired outputs from
00620 each table which correspond to all possible phonemic inputs. The
00630 signature tables are then processed.
00640
00650 The processing of each table is done in two steps, one
00660 process at each entry to the table and the second only periodically.
00670 The first process consists of locating a single entry line within the
00680 table as specified by the inputs to the table and adding a 1 to the
00690 appropriate field to indicate the presence of the property specified
00700 by hint table as corresponding to the phoneme specified in the
00710 phonemic transcription. At this time a report is also made as to the
00720 table's output as determined from the averaged results of previous
00730 learning so that a running record may be kept of the performance of
00740 the system. At periodic intervals all tables are updated to
00750 incorporate recent learning results. To make this process easily
00760 understandable, let us restrict our attention to a table used to
00770 identify a single significant feature say Voicing. The hint table
00780 will identify whether or not the phoneme currently being processed is
00790 to be considered voiced. If it is voiced, a 1 is added to the "yes"
00800 field of the entry line located by the normal inputs to the table. If
00810 it is not voiced, a 1 is added to the "no" field. At updating time
00820 the output that this entry will subsequently report is determined by
00830 dividing the accumulated sum in the "yes" field by the sum of the
00840 numbers in the "yes" and the "no" fields, and reporting this quantity
00850 as a number in the range from 0 to 7. Actually the process is a bit
00860 more complicated than this and it varies with the exact type of table
00870 under consideration, as reported in detail in appendix B. Outputs
00880 from the signature tables are not probabilities, in the strict sense,
00890 but are the statistically-arrived-at odds based on the actual
00900 learning sequence.
00910
00920 The preamble of the table has space for storing twelve past
00930 outputs. An input to a table can be delayed to that extent. This table
00940 relates outcomes of previous events with the present hint-the
00950 learning input. A certain amount of context dependent learning is thus
00960 possible with the limitation that the specified delays are constant.
00970
00980 The interconnected hierarchy of tables form a network which
00990 runs increamentally, in steps synchronous with time window over which
01000 the input signal is analised. The present window width is set at 12.8
01010 ms.(256 points at 20 K samples/sec.) with overlap of 6.4 ms. Inputs
01020 to this network are the parameters abstracted from the frequency
01030 analyses of the signal, and the specified hint. The outputs of the
01040 network could be either the probability attached to every phonetic
01050 symbol or the output of a table associated with a feature such as
01060 voiced, vowel etc. The point to be made is that the output generated
01070 for a sample is essentially independent of its contiguous
01080 samples. The dependency achieved by using delayes in the inputs is
01090 invisible to the outputs. The outputs thus report the best estimate on
01100 what the current acoustic input is with no relation to the past
01110 outputs. Relating the successive outputs along the time dimension is
01120 realised by counters.
01130
01140 The Use of COUNTERS
01150
01160 The transition from initial sample space to segment space is
01170 made posible by means of COUNTERS which are summed and reiniated
01180 whenever their inputs cross specified threshold values, being
01190 triggered on when the input exceeds the threshold and off when it
01200 falls below. Momentary spikes are eliminated by specifying time
01210 hysteresis, the number of consecutive samples for which the input
01220 must be above the threshold. The output of a counter provides
01230 information about starting time, duration and average input for the
01240 period it was active.
01250
01260 Since a counter can reference a table at any level in the
01270 hierarchy of tables, it can reflect any desired degree of information
01280 reduction. For example, a counter may be set up to show a section of
01290 speech to be a vowel, a front vowel or the vowel /I/. The counters can
01300 be looked upon to represent a mapping of parameter-time space into a
01310 feature-time space, or at a higher level symbol-time space. It may be
01320 useful to carry along the feature information as a back up in those
01330 situations where the symbolic information is not acceptable to
01340 syntactic or semantic interpretation.
01350
01360 In the same manner as the tables, the counters run completely
01370 independent of each other. In a recognition run the counters may
01380 overlap in arbitrary fashion, may leave out gaps where no counter has
01390 been triggered or may not line up nicely. A properly segmented output,
01400 where the consecutive sections are in time sequence and are neatly
01410 labled, is essential for processing it further. This is achieved by
01420 registering the instants when the counters are triggered or
01430 terminated to form time slices called segments.
01440
01450 An event is the period between successive activation or
01460 termination of any counter. An event shorter than a specified time is
01470 merely ignored. A record of event durations and upto three active
01480 counters, ordered according to their probability, is maintained.
01490
01500 An event resulting from the processing described so far,
01510 represents a phonette - one of the basic speech categories defined as
01520 hints in the learning process. It is only an estimate of closeness to
01530 a speech category , based on past learning. Also each category has a
01540 more-or-less stationary spectral characterisation. Thus a category may
01550 have a phonemic equivalent as in the case of vowels , it may be
01560 common to phoneme class as for the voiced or unvoiced stop gaps or it
01570 may be subphonemic as a T-burst or a K-burst. The choices are based on
01580 acoustic expediency, i.e. optimisation of the learning rather than
01590 any linguistic considerations. However a higher level interpretive
01600 programs may best operate on inputs resembling phonemic
01610 trancription. The contiguous segments may be coalesced into phoneme like
01620 units using diadic or triadic probabilities and acoustic-phonetic
01630 rules particular to the system. For example, a period of silence
01640 followed by a type of burst or a short friction may be combined to
01650 form the corresponding stop. A short friction or a burst following a
01660 nasal or a lateral may be called a stop even if the silence period is
01670 short or absent. Clearly these rules must be specific to the system,
01680 based on the confidence with which durations and phonette categories
01690 are recognised.
00010 Appendix 3
00020 SPEECH RESEARCH AT STANFORD UNIVERSITY
00030
00040 Efforts to establish a vocal communicatton link with a
00050 digital computer have been underway at Stanford since 1963.
00060 These efforts have been primarily concerned with four areas of
00070 research. First, basic research in extracting phonemic and
00080 linguistic information from speech waveforms has been persued.
00090 Second, the application of automatic learning processes have
00100 been investigated. Third, the use of syntax and semantics to
00110 aid speech recoginition have been explored. Finally,
00120 the application of speech recognition systems to control other
00130 processes developed at the Artifical Intelligence Facility have
00140 been carried out. These efforts have been carried out in
00150 parallel with varying emphasis on particular factors at
00160 different times. None of the facets of this research have been
00170 solved completely. However, each limited success has provided
00180 insight and direction which opened a wealth of challenging,
00190 state of the art, reseach projects.
00200
00210 The fruits of Stanford's speech research program were
00220 first seen in October 1964 when Raj Reddy published a report
00230 describing his preliminary investigations on the analysis of
00240 speech waveforms [1]. This report described the intial digital
00250 processes developed for analyzing waveforms of vowels and
00260 consonants, fundamental frequency, and formants. These
00270 processes were used as the basis for a simple vowel recognition
00280 system and synthesis of sounds.
00290
00300 By 1966 Reddy had build a much larger system which obtained a
00310 phonemic transcription and which achieved segmentation of
00320 connected phrases utilizing hypotheses testing [2]. This
00330 system represented a signficant contribution towards speech
00340 sound segmentation [3]. This system operated on a subset of the
00350 speech of a single cooperative speaker.
00360
00370 In 1967 Reddy and his students had refined several of
00380 his processes and published papers on phoneme grouping for
00390 speech recognition [4], pitch period determination of speech
00400 sounds [5], and computer recognition of connected speech [6].
00410 At this time Reddy was considering the introduction of learning
00420 into his processes at several stages. He was also supervising
00430 several related student projects including limited vocabulary
00440 speech recognition, a phoneme string to word string
00450 transcription program, a syllable junction program, and
00460 telephone speech recognition.
00010 1968 was an extremely productive year for Professor
00020 Reddy and his speech group. Pierre Vicens published a report
00030 on preprocessing for speech analysis [7]; Reddy published a
00040 paper on the computer transcription of phonemic symbols [8];
00050 Reddy and Ann Robinson published a paper on phoneme-to-grapheme
00060 translation of English [9]; Reddy and Vicens published a paper
00070 on procedures for segmentation of connected speech [10]; and
00080 Reddy presented a paper in Japan on consonantal clustering and
00090 connected speech recognition [11]. In addition to this basic
00100 speech research, a paper by John McCarthy , Lester Earnest, Raj
00110 Reddy, and Pierre Vicens was presented at the 1968 Fall Joint
00120 Computer Conference entitled "A Computer With Hands, Eyes, and
00130 Ears" which, in part, described the vocal control of the
00140 artifical arm developed at Stanford [12].
00150
00160 By 1969 the Stanford developed speech processes were
00170 successfully segmenting and parsing continuous utterances from
00180 a restricted syntax. Pierre Vicens produced a report on
00190 aspects of speech recognition by computer which investigated
00200 the techniques and methodologies which are useful in achieving
00210 close to real-time recognition of speech [13]. In March of
00220 1969, Raj Reddy, Dave Espar, and Art Eisenson produced a 16mm
00230 color movie with sound entitled "Hear Here". This film
00240 described the state of the speech recognition project as of
00250 Spring, 1969. In addition, Raj Reddy completed a report on the
00260 use of environmental, syntactic, and probabilistic constraints
00270 in vision and speech [14] and Reddy and R.B. Neely reported
00280 their research on the contextual analysis of phonemes of
00290 English [15].
00300
00310 In 1970, a paper was presented by Raj Reddy, L. D.
00320 Erman, and R. B. Neely concerning the speech recognition
00330 project at the IEEE Systems Science and Cybernetics Conference.
00340 At this time Professor Reddy left Stanford to join the faculty
00350 of Carneige-Mellon University and Dr. Arthur Samuel became
00360 the head of the Stanford speech research efforts. Dr. Samuel
00370 was the developer of an extremely successful machine learning
00380 scheme which had previously been applied to the game of
00390 checkers [16],[17]. He resolved to apply them to speech
00400 recognition.
00010 By 1971 the first report on a speech recognition system
00020 utilizing Samuel's learning scheme was written by George White
00030 [18]. This report was primarily concerned with the examination
00040 of the properties of signature trees and the heuristics
00050 involved in their application to an optimal minimal set of
00060 features to achieve recognition. Also at this time, M.M.
00070 Astrahan produced a report describing his research on speech
00080 analysis by clustering, or the hyperphoneme method [19]. This
00090 process attempted to do speech recognition by mathematical
00100 classifications instead of the traditional phonemes or
00110 linguistic categories. This was accomplished by
00120 nearest-neighbor classification in a hyperspace wherein cluster
00130 centers, or hyperphonemes, had been established.
00140
00150 In 1972 R.B.Thosar and A.L. Samuel presented a report
00160 concerning some preliminary experiments in speech recognition
00170 using signature tables [20]. This approach represented a
00180 general attack on speech recognition employing learning
00190 mechanisms at each stage of classification.
00200
00210 The speech effort in 1973 has been devoted to two
00220 areas. First, a mathematically rigorous examination and
00230 improvement of the signature table learning mechanism has been
00240 accomplished by R.B. Thosar. Second, a segmentation scheme
00250 based on signature tables is being developed to provide
00260 accurate segmentation together with probabilities or confidence
00270 values for the most likely phoneme occuring during each
00280 segment. This process attempts to extract as much information
00290 about an acoustic signal as possible and to pass this
00300 information to higher level processes. The preliminary results
00310 of this segmentation scheme will be presented at the speech
00320 segmentation workshop to be held in July at Carneige-Mellon
00330 University. In addition to these activities, a new, high speed
00340 pitch detection scheme has been developed by J. A. Morrer and
00350 has been submitted for publication.
00010 BIBLIOGRAPHY
00020
00030 1. AIM-26, Raj Reddy, Experiments on Automatic Speech
00040 Recognition by a Digital Computer, October 1964, 19 pages.
00050
00060 2. AIM-43, Raj Reddy, An Approach to Computer Speech
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00180
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00210
00220 7. AIM-71, Pierre Vicens, Preprocessing for Speech
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00240
00250 8. D. Reddy, "Computer Transcription of Phonemic Symbols",
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00280 9. D. Reddy, and Ann Robinson, "Phoneme-To-Grapheme
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00310
00320 10. D. Reddy, and P. Vicens, "Procedures for Segmentation
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00340
00350 11. D. Reddy, "Consonantal Clustering and Connected Speech
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00370 Vo. 2, pp. C-57 to C-60, Tokyo, 1968.
00380
00390 12. John McCarthy, Lester Earnest, D. Raj Reddy, and
00400 Pierre Vicens, "A Computer With Hands, Eyes, and Ears",
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00010 13. AIM-85, Pierre Vicens, Aspects of Speech Recognition by
00020 Computer, April 1969, 210 pages.
00030
00040 14. AIM-78, D. Raj Reddy, On the Use of Environmental,
00050 Syntactic and Probabilistic Constraints in Vision and Speech,
00060 January 1969, 23 pages.
00070
00080 15. AIM-79, D. R. Reddy and R. B. Neely, Contectual
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00100
00110 16. A. L. Samuel, "Some Studies in Machine Learning Using
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00140 17. A. L. Samuel, "Some Studies in Machine Learning Using
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00170
00180 18. AIM-136, George M. White, Machine Learning Through
00190 Signature Trees. Applications to Human Speech, October 1870, 40
00200 pages.
00210
00220 19. AIM-124, M. M. Astrahan, Speech Analysis by
00230 Clustering, or the Hyperphoneme Method, June 1970, 22 pages.
00240
00250 20. R. B. Thosar and A. L. Samuel, Some Preliminary
00260 Experiments in Speech Recognition Using Signature Table
00270 Learning, ARPA Speech Understanding Research Group Note 43.
00280 21. R.B.Thosar, @stimation of Probability Densities using
00290 Signature Tables for Application to Pattern Recognition, ARPA
00300 Speech Understanding Research Group Note 81.