perm filename ZZZ[1,ALS]1 blob
sn#001059 filedate 1972-08-28 generic text, type T, neo UTF8
00010 Some Preliminary Experiments in Speech Recognition
00020 using Signature Table Learning
00030
00040 by
00050 R.B.Thosar and A.L.Samuel
00060
00070 A limited amount of success has been achieved in the
00080 application of the signature table scheme of machine
00090 learning to the problem of automatic speech recognition.
00100 The scheme is based on the assumption that the recognition
00110 system must eventually employ a learning mechanism and that
00120 the acoustic part of the system must start by dealing
00130 with the recognition of fairly elemental speech segments
00140 rather than with words if it is to have general utility.
00150
00160
00170
00180 The experiments here reported are only the early beginning
00190 part of a long range program to devise elements of a speech
00200 recognition system that would not be dependent upon the use of a
00210 limited vocabulary and that could recognize continuous speech by a
00220 number of different speakers. The aim is that the system should be
00230 able to function successfully either without any previous training
00240 for the specific speaker in question or after a short learning
00250 session in which the speaker would be asked to repeat certain phrases
00260 designed to train the system on those phonetic utterances that seemed
00270 to depart from the previously learned norm.
00280
00290 At the present time we are not attempting to build a complete
00300 operating system. Rather, the attempt is to concentrate on those
00310 aspects of the general speech recognition problem that seem to
00320 require the greatest amount of work or that are within our particular
00330 field of competance. It is hoped that our work will, in this way,
00340 supplement the work on complete systems that is underway at a number
00350 of different locations.
00360
00370 We are currently attempting to apply the signature table
00380 learning scheme, previously used in another connection(1), to the
00390 problem of phoneme identification. This scheme makes use of a
00400 hierarchy of tables that contain identifying information which has
00410 been derived from learning sessions, as will be described below. By
00420 restricting all of the speech-specific aspects to data stored in
00430 tables, the instruction sequence that processes these tables is
00440 independent of the nature of the information being processed. The
00450 same routine that processes those tables whose inputs are derived
00460 from the acoustic input can also be used to process tables with
00470 syntactic,semantic and linguistic inputs as well. Signature tables
00480 are thus viewed as a basic tool that deserves special study.
00490
00500 Signature tables can be used to perform four essential
00502
00504
00506 (1)
00508
00010 functions that are required in the automatic recognition of speech.
00020 These functions are: (1) the elimination of superfluous and
00030 redundant information information from the acoustic input stream, (2)
00040 the transformation of the remaining information from one coordinate
00050 system to a more phonetically meaningful coordinate system, (3) the
00060 mixing of acoustically derived data with syntactic, semantic and
00070 linguistic information to obtain the desired recognition, and (4) the
00080 introduction of a learning mechanism. Signature tables differ from
00090 the perceptron to which they are often erroneously compared in that
00100 all possible functions of the inputs can be represented and reported
00110 as the output, subject only to the restriction imposed by the digital
00120 nature and limited range of the permited output. A hierarchy of
00130 tables thus provides a mechanism for the systematic reduction in
00140 information content by the elimination of extraneous and redundant
00150 information. If the hierarchy is properly designed one can expect
00160 that this reduction in total information will be obtained without any
00170 loss in the desired information. Obviously considerable care must be
00180 exercised in the design of the tables and of the hierarchy to achieve
00190 this desirable objective. We are attempting to do this by designing
00200 the signature table hierarchy along conventional phonetic lines so
00210 that we can take full advantage of the wealth of phoneticly oriented
00220 research that has been and is being done in many places.
00230
00240 The signature tables, as used in speech recognition, differ
00250 in a number of significant respects from the tables previously
00260 described.
00270
00280 A signature table consists of two parts, a preamble and the
00290 table proper. The preamble contains: (1) space for saving a record of
00300 the current and recent output reports from the table, (2) identifying
00310 information as to the specific type of table, (3) a parameter that
00320 identifies the desired output from the table and that is used in the
00330 learning process, (4) a gating parameter specifying the input, that
00340 is to be used to gate the table, (6) the gating level to be used and
00350 (7) parameters that identify the sources of the normal inputs to the
00360 table.
00370
00380 All inputs are limited in range and specify either the
00390 absolute level of some basic property or more usually the probability
00400 of some property being present. These inputs may be from the
00410 original acoustic input or they may be the outputs of other tables.
00420 If from other tables they may be for the current time step or for
00430 earlier time steps, (subject to practical limits as to the number of
00440 time steps that are saved).
00450
00460 The output, or outputs, from each table are similarly limited
00470 in range and specify, in all cases, a probability that some
00480 particular significant feature, phonette, phoneme, word segment, word
00490 or phrase is present.
00500
00502
00504
00506 (2)
00508
00010 We are limiting the range of inputs and outputs to values
00020 specified By 3 bits and the number of entries per table to 64
00030 although this choice of values is a matter to be determined by
00040 experiment. We are also providing for any of the following input
00050 combinations, (1) one input of 6 bits, (2) two inputs of 3 bits each,
00060 (3) three inputs of 2 bits each, and (4) six inputs of 1 bit each.
00070 The uses to which these differint forms are put will be described
00080 later.
00090
00100 The body of each table contains entries corresponding to
00110 every possible combination of the allowed input parameters. Each
00120 entry in the table actually consists of several parts. There are
00130 fields assigned to accumulate counts of the occurrances of incidents
00140 in which the specifying input values coincided with the different
00150 desired outputs from the table as found during previous learning
00160 sessions and there are fields containing the summarized results of
00170 these learning sessions, which are used as outputs from the table.
00180 The outputs from the tables can then express to the allowed accuracy
00190 all possible functions of the input parameters.
00200
00210 When operating in the learning mode the program is supplied
00220 with a sequence of stored utterances with accompanying phonetic
00230 transcriptions. Each segment of the incoming speech signal is
00240 analysed (Fourier transforms or inverse filter equivalent) to obtain
00250 the necessary input parmeters for the lowest level tables in the
00260 signature table hierarchy. At the same time reference is made to a
00270 table of phonetic "hints" which prescribe the desired outputs from
00280 each table which correspond to all possible phonemic inputs. The
00290 signature tables are then processed.
00300
00310 The processing of each table is done in two steps, one
00320 process at each entry to the table and the second only periodically.
00330 The first process consists of locating a single entry line within the
00340 table as specified by the inputs to the table and adding a 1 to the
00350 appropriate field to indicate the presence of the property specified
00360 by hint table as corresponding to the phoneme specified in the
00370 phonemic transcription. At this time a report is also made as to the
00380 table's output as determined from the averaged results of previous
00390 learning so that a running record may be kept of the performance of
00400 the system. At periodic intervals all tables are updated to
00410 incorporate recent learning results. To make this process easily
00420 understandable, let us restrict our attention to a table used to
00430 identify a single significant feature say Voicing. The hint table
00440 will identify whether or not the phoneme currently being processed is
00450 to be considered voiced. If it is voiced, a 1 is added to the "yes"
00460 field of the entry line located by the normal inputs to the table. If
00470 it is not voiced, a 1 is added to the "no" field. At updating time
00480 the output that this entry will subsequently report is determined by
00490 dividing the accumulated sum in the "yes" field by the sum of the
00500 numbers in the "yes" and the "no" fields, and reporting this quantity
00502
00504
00506 (3)
00508
00010 as a number in the range from 0 to 7. Actually the process is a bit
00020 more complicated than this and it varies with the exact type of table
00030 under consideration, as reported in detail in appendix B. Outputs
00040 from the signature tables are not probabilities, in the strict sense,
00050 but are the statistically-arrived-at odds based on the actual
00060 learning sequence.
00070
00080 The preamble of the table has space for storing tweive past
00090 outputs. An input to a table can be delayed to that extent.This table
00100 relates outcomes of previous events with the present hint-the
00110 learning input.A certain amount of context dependent learning is thus
00120 possible with the limitation that the specified delays are constant.
00130
00140 The interconnected hierarchy of tables form a network which
00150 runs increamentally, in steps synchronous with time window over which
00160 the input signal is analised.The present window width is set at 12.8
00170 ms.(256 points at 20 K samples/sec.) with overlap of 6.4 ms. Inputs
00180 to this network are the parameters abstracted from the frequency
00190 analyses of the signal, and the specified hint.The outputs of the
00200 network could be either the probability attached to every phonetic
00210 symbol or the output of a table associated with a feature such as
00220 voiced,vowel ect.The point to be made is that the output generated
00230 for a segment is essentially independent of its contiguous
00240 segments.The dependency achieved by using delayes in the inputs is
00250 invisible to the outputs.The outputs thus report the best estimate on
00260 what the current acoustic input is with no relation to the past
00270 outputs.Relating the successive outputs along the time dimension is
00280 realised by counters.
00290
00300 A counter provides a mechanism for indicating when an output
00310 reaches sigificant level and the period for which it remains high.A
00320 counter is triggered when its input crosses a specified
00330 threshold.Momentary spikes are eliminated by specifying time
00340 hysteresis, the numBer of consecutive segments for which the input
00350 must be above the threshold.The output of a counter provides
00360 information about starting time,duration and average input for the
00370 period it was active.
00380
00390 Since a counter can reference a table at any level in the
00400 hierarchy of tables, it can reflect any desired degree of information
00410 reduction. For example, a counter may be set up to show a section of
00420 speech to be a vowel,a front vowel or the vowel /I/.The counters can
00430 be looked upon to represent a mapping of parameter-time space into a
00440 feature-time space, or at a higher level symbol-time space.It may be
00450 useful to carry along the feature information as a back up in those
00460 situations where the symbolic information is not acceptable to
00470 syntactic or semantic interpretation.
00480
00490 In the same manner as the tables, the counters run completely
00500 independent of each other.In a recognition run the counters may
00502
00504
00506 (4)
00508
00010 overlap in arbitrary fashion, may leave out gaps where no counter has
00020 been triggered or may not line up nicely.A properly segmented output,
00030 where the consecutive sections are in time sequence and are neatly
00040 labled, is essential for processing it further.This is achieved by
00050 registering the instants when the counters are triggered or
00060 terminated to form time segments called events.
00070
00080 An event is the period between successive activation or
00090 termination of any counter.An event shorter than a specified time is
00100 merely ignored. A record of event durations and upto three active
00110 counters, ordered according to their probability, is maintained.
00120
00130 An event resulting from the processing described so far,
00140 represents a phone or a phonette-the basic speech categories defined
00150 as hints in the learning process. It is only an estimate of closeness
00160 to a speech category , based on past learning.Also each category has
00170 a more-or-less stationary spectral characterisation.Thus a category
00180 may have a phonemic equivalent as in the case of vowels , may be
00190 common to phoneme class as for the voiced or unvoiced stop gaps or
00200 may be subphonemic as a T-burst or a K-burst.They should be and are
00210 based on acoustic expediency, i.e. optimisation of the learning
00220 rather than any linguistic considerations.However a higher level
00230 interpretive programs may best operate on inputs resembling phonemic
00240 trancription.The contiguous events may be coalesced into phoneme like
00250 units using diadic or triadic probabilities and acoustic-phonetic
00260 rules particular to the system.For example, a period of silence
00270 followed by a type of burst or a short friction may be combined to
00280 form the corrosponding stop.A short friction or a burst following a
00290 nasal or a lateral may be called a stop even if the silence period is
00300 short or absent.Clearly these rules must be specific to the system,
00310 based on the confidence with which durations and phonette categories
00320 are recognised.
00330
00340 How far can such an bottom-up approach be pushed? In absence
00350 of a higer interpretive program the first order estimate generated by
00360 the acoustic processor cannot be improved upon.The system however
00370 does provide several levels of backup if its output is not acceptable
00380 to the interpreter.The lower order events and the feature space are
00390 available for immidiate queries.If the interpreter has high
00400 expectation for a class, say nasals, for a segment of speech, a rerun
00410 can force a choice with suitable modification of gating thresholds
00420 for selected tables and counters. Thus no basic modification in the
00430 system seems necessary to provide a backtrack capability - probably
00440 an important requirement in a complete speech recognition system.
00450
00460 Foregoing, rather long introduction was intended to convey
00470 the methodology we have adopted in tackling the speech recognition
00480 problem. So far the system has all the machinary required for
00490 learning and generating event sequences for arbitrary speech inputs,
00500 and for the evaluation of the learning and recognition processes.The
00502
00504
00506 (5)
00508
00010 phonette categories and the table network is constucted along the
00020 traditional phonetic approach.The final setup may well reflect a
00030 categorisation which produces statistically optimal result.
00040 Operational details of the current system and some preliminary
00050 results are included in the following appendices.
00060
00070 Appendix A describes routines for parameter extraction from
00080 the fourier and linear prediction analysis.
00090
00100 Appendix B describes the signature tables.
00110
00120 Appendix C describes the present table network and some of
00130 the results.
00140
00150
00160
00170
00180
00190
00200
00210
00220
00230
00240
00250
00260
00270
00280
00290
00300
00310
00320
00330
00340
00350
00360
00370
00380
00390
00400
00410
00420
00430
00440
00450
00460
00470
00480
00490
00500
00502
00504
00506 (6)
00508
00010 APPENDIX A
00020
00030 Extraction of Speech Parameters
00040
00050
00060 The acoustic signal is analised in regular 12.8 ms. steps
00070 with an overlap of 6.4 ms.Each segment is multiplied by the Hamming
00080 window and a standerd FFT or a linear prediction algorithm is applied
00090 to obtain a log-magnitude spectrum. The FFT routine is faster MACRO
00100 version of a FORTRAN program developed at the University of Utah. The
00110 linear prediction scheme uses Markel's code.
00120
00130 A uniform procedure is applied to the spectrum to derive a
00140 set of 19 parameters. These include the first three formants, nasal
00150 and fricative poles and zeros, the amplitudes at the corrosponding
00160 points and average energies in selected frequency regions.Most of the
00170 poles and zeros are located as peaks and minimas in a specified
00180 frequency range.A slightly more complicated procedure is used to
00190 determine the three formants for which the ranges are allowed to
00200 overlap.
00210
00220 The ambiguous situation where the second formant is confused
00230 with either the first or the third formant is resolved using two
00240 conditions. If another "prominent" peak is found in the range, it is
00250 labeled as second formant. Otherwise the spectral balance given by
00260 ratio of energies in F1 and F3 regions is used to place the second
00270 formant close to F1 or F3.
00275
00280 The average energy is obtained by averaging over the entire
00290 frequency range. In order to avoid sharp changes in the low and high
00300 frequency energies , the magnitude is linearly reduced from the
00310 "break-point" to the "cutoff-point".
00320 The nomenclature and the parameter ranges are given in Table 1.
00325
00330 The linear prediction scheme uses identical parameter ranges
00340 and almost the same procedures. The iterative peak locating procedure
00350 is somewhat simplified since a much cleaner spectrum without any
00360 local irregularity, is obtained from linear prediction.
00370
00380
00390
00400
00410
00420
00430
00440
00450
00460
00470
00480
00490
00500
00510
00520
00530 (7)
00540
00010 Parameter Lower Limit Upper Limit (Hz)
00020
00030 F1: first formant 200 800
00040 F2: second formant 700 2050
00050 F3: third formant 2000 3200
00060 A1: F1 amplitude
00070 A2: F2 amplitude
00080 A3: F3 amplitude
00090 FP1: fricative pole 1 1800 3200
00100 FP2: fricative pole 2 3200 5000
00110 FP1A: FP1 amplitude
00120 FP2A: FP2 amplitude
00130 FZ: fricative zero FP1 FP2
00140 FZA: FZ amplitude
00150 NP: nasal pole 800 1500
00160 NZ: nasal zero NP NP+500
00170 NPA: NP amplitude
00180 NZA: NZA amplitude
00190 LPE: low region energy 0 450
00200 HPE: high region energy 2500 10000
00210 AVE: average energy 0 10000
00220
00230
00240 Table 1. Input Parameters and Their Ranges
00250
00260
00270
00280
00290
00300
00310
00320
00330
00340
00350
00360
00370
00380
00390
00400
00410
00420
00430
00440
00450
00460
00470
00480
00490
00500
00502
00504
00506 (8)
00508
00010 APPENDIX B
00020
00030 Signature Tables
00040
00050
00060 The signature tables are basically three types: (1) Input
00070 tables are designed to compress a parameter range and make it
00080 compatible with the rest of the system. (2) Intermediate P-tables
00090 learn on a single feature and have a single output. (3) Output
00100 Q-tables learn on a set of phonetically similar sounds, upto four per
00110 table, and have the corresponding outputs and a fifth null output.
00120
00130 As mentioned earlier, each table is 64 words long with a ten
00140 word preamble , exept that a Q table is twice as long to provide
00150 space for the five fields.
00160
00170 Every line in an input table has two fields, one contains the
00180 count of the number of times that particular input occured and the
00190 other contains the output value associated with that count. The
00200 parameter range is adjusted to be 0:63 so that it's value can be used
00210 directly to locate the proper table address. The computation of the
00220 line outputs is done so as to maximise the information content in the
00230 output,i.e. every output value has equal probability of occurence.
00240 This is realised by dividing up the table in eight sections such that
00250 sum of the counts in each section is almost 1/8th of the total count.
00260 This updating is done after eight learning inputs in early stages and
00270 less frequently afterwards.
00280
00290 The P tables can have two 3-bit inputs, three 2-bit inputs or
00300 six 1-bit inputs. The least significant bits of the 3-bit table
00310 output usually generated, are ignored wherever necessary. The joint
00320 six bits thus provide the correct address within the table. The
00330 gating input to a table is obtained from another table.The gating
00340 level can be a positive or a negative number between 0 and 7.A
00350 positive threshold turns a gate on if the gating input is above the
00360 threshold. Converse is true for a negative threshold.Every line in a
00370 table has two fields which accumulate counts and a field for the
00380 output.A 1 is added to good count (G) field if the learning input is
00390 on otherwise a 1 is added to the bad count (B) field. The
00400 corresponding outputs are computed by normalising the ratios G/G+B
00410 into 0 to 7 range.
00420
00430 The input and gating configuration for the Q tables is
00440 identical to the P tables.Each line of the Q table has five count
00450 fields, four for the learning inputs and the fifth for "not-any".
00460 Only the positive counts are kept.If none of the learning inputs is
00470 present the not-any field is increamented.The output corresponding to
00480 a hint is computed on the basis of the counts in other fields in the
00490 same line.
00500
00510
00520 (9)
00530
00010
00020 The learning network is created by a program called MAKE. The
00030 tables are assigned consecutive 74 word blocks in the space allocated
00040 for the tables. The first word in the preamble is reserved for
00050 current and past outputs of the table. An input to the table is thus
00060 merely a byte pointer pointing to the correct table. The byte pointer
00070 is itself stored in the body of the preamble.The code running the
00080 tables is thus greatly simplified.
00090
00100 Execution of the tables is identical in learning as well as
00110 recognition modes except that no counts are added in the recognition
00120 mode.This simplifies the code even further.
00130
00140
00150
00160
00170
00180
00190
00200
00210
00220
00230
00240
00250
00260
00270
00280
00290
00300
00310
00320
00330
00340
00350
00360
00370
00380
00390
00400
00410
00420
00430
00440
00450
00460
00470
00480
00490
00500
00502
00504
00506 (10)
00508
00010
00020 APPENDIX C
00030
00040 Current System
00050
00060
00070 The current system is probably best described with respect to
00080 the documentation generated by the program MAKE when the system is
00090 created. The documentation is given as Tables 1-4 in this appendix.
00100
00110 The number of acoustic input parameters, their mnemonic
00120 representation and the order in which they are supplied is determined
00130 by the parameter extraction routine. MAKE generates an input table
00140 for each parameter. Henceforth the table is referenced by its
00150 mnemonic tag. A list of current tables appears in Table 1.
00160
00170 The system allows upto 36 significant features to be
00180 specified. The present set of features is given in Table 2. The list
00190 of sound categories with their associated significant features is
00200 also in Table 2. Most of the features retain their conventional
00210 significance.Rather artificial features like VOC1,VOC2 were
00220 introduced to separate members of subclass such as front vowels.
00230
00240 The features have been chosen so as to isolate a subset of
00250 phonettes which can be consistently identified. Features which may
00260 define a large subset have been avoided for two reasons.First, it may
00270 tend to smear a table reducing its effeciveness.Second, the subset
00280 defined by negation of a feature, say ¬VOWEL, appears to be a weaker
00290 decision by a table.
00300
00310 The signature tables currently in use are given in Table
00320 3.Dummy tables are inserted between two levels of the tables for
00330 later additions.The number attached to the table type gives the
00340 number of inputs. The number adjacent to an input shows the delay.
00350 The first value attached to a gating input is the threshold.
00360
00370 The set of counters that are being used is given in Table 4.
00380
00390
00400 The data being used for experimentation with the system is a
00410 set of 54 words recorded by Dr. Ken Stevens. These recordings have
00420 been used earlier by Gold, Bobrow and Klatt, Vicens, and Erman and
00430 Reddy. Only a limited amount of learning has been done, and no
00440 attempt has been made to modify the set of phonetts or the tables to
00450 improve performance.
00460
00470
00480
00490 RESULTS-------
00500
00010 SIGNATURE TABLE SET-UP AS OF 24-JUL-1972 1421:49
00020
00030 The following input tables exist
00040
00050 F1 F2 F3 A1 A2 A3 FP1 FP1A
00060 FP2 FP2A FZ FZA NP NPA NZ NZA
00070 LPE AVE HPE
00080
00100 Table 1. List of Input Parameters.
00110
00120
00140 Available SIGNIFICANT FEATURES are
00150
00160 VOICED FRIC VOWEL GLIDE NASAL STOP BURST FRONT
00170 MID BACK VOC1 VOC2 GLI1 GLI2 NAS1 NAS2
00180 VF1 VF2 FRIC1 FRIC2 FRIC3 FRICX NASGLI VOIFRI
00190
00210 PH list and H list table contains
00220
00230 PH Significant features
00240 NU
00250 EE VOICED VOWEL FRONT
00260 AE VOICED VOWEL FRONT VOC1
00270 E VOICED VOWEL FRONT VOC2
00280 I VOICED VOWEL FRONT VOC1 VOC2
00290 AS VOICED VOWEL MID
00300 AA VOICED VOWEL MID VOC1
00310 AR VOICED VOWEL MID VOC2
00320 A VOICED VOWEL MID VOC1 VOC2
00330 OO VOICED VOWEL BACK
00340 U VOICED VOWEL BACK VOC1
00350 AW VOICED VOWEL BACK VOC2
00360 O VOICED VOWEL BACK VOC1 VOC2
00370 Y VOICED GLIDE GLI1 NASGLI
00380 R VOICED GLIDE NASGLI
00390 L VOICED GLIDE GLI1 GLI2 NASGLI
00400 W VOICED GLIDE GLI2 NASGLI
00410 NG NASAL NASGLI
00420 M VOICED NASAL NAS1 NASGLI
00430 N VOICED NASAL NAS2 NASGLI
00440 F FRIC FRIC1
00450 S FRIC FRIC2
00460 SH FRIC FRIC3
00470 H FRIC
00480 V VOICED FRIC VF1 VOIFRI
00490 Z VOICED FRIC VF2 VOIFRI
00500 ZH VOICED FRIC VOIFRI
00510 PB FRIC BURST FRIC1
00520 TB FRIC BURST FRIC2
00530 KB FRIC BURST FRIC3
00540 SI STOP
00550 VS VOICED STOP
00560
00570 Table 2. List of Phonettes and Associated Features. (12)
00010 The following tables exist
00020
00030 Name TYPE Learn Gate IN1 IN2 IN3 IN4 IN5 IN6
00040 VOICED P2 VOICED 0 0LPE 0LPE 0HPE
00050 VOWEL P2 VOWEL 4 0VOI 0LPE 0AVE
00060 DUM1 P2 0 0 0 0
00070 FRIC1 P2 FRIC 11 0VO 0LPE 0HPE
00080 STOP1 P3 STOP 11 0VO 0A1 0A2 0AVE
00090 DUM2 P2 0 0 0 0
00100 DUM3 P2 0 0 0 0
00110 FRIC P2 FRIC 11 0VO 0FRIC1 0AVE
00120 T5 P3 FRONT 4 0VOW 0F1 0F2 0F3
00130 T6 P3 FRONT 4 0VOW 0A1 0A2 0A3
00140 T8 P3 MID 4 0VOW 0F1 0F2 0F3
00150 T9 P3 MID 4 0VOW 0A1 0A2 0A3
00160 T11 P3 BACK 4 0VOW 0F1 0F2 0F3
00170 T12 P3 BACK 4 0VOW 0A1 0A2 0A3
00180 VOC1 P3 VOC1 4 0VOW 0F1 0F2 0A2
00190 VOC2 P3 VOC2 4 0VOW 0F1 0F2 0A2
00200 DUM4 P2 0 0 0 0
00210 DUM5 P2 0 0 0 0
00220 BURST P2 BURST 3 0FRI 0AVE 0HPE
00230 FRN P2 FRONT 4 0VOW 0T5 0T6
00240 MID P2 MID 4 0VOW 0T8 0T9
00250 BCK P2 BACK 4 0VOW 0T11 0T12
00260 T17 P3 FRIC1 4 0FRI 0FP1 0FP2 0FZ
00270 T18 P3 FRIC1 4 0FRI 0FP1A 0FP2A 0FZA
00280 T20 P3 FRIC2 4 0FRI 0FP1 0FP2 0FZ
00290 T21 P3 FRIC2 4 0FRI 0FP1A 0FP2A 0FZA
00300 T23 P3 FRIC3 4 0FRI 0FP1 0FP2 0FZ
00310 T24 P3 FRIC3 4 0FRI 0FP1A 0FP2A 0FZA
00320 STOP P3 STOP 11 0VO 0STOP1 0FRIC 0LPE
00330 DUM6 P2 0 0 0 0
00340 DUM7 P2 0 0 0 0
00350 FRNSS P3 FRONT 4 0VOW 0FRN 1FRN 2FRN
00360 MIDSS P3 MID 4 0VOW 0MID 1MID 2MID
00370 BCKSS P3 BACK 4 0VOW 0BCK 1BCK 2BCK
00380 FR1 P2 FRIC1 4 0FRI 0T17 0T18
00390 FR2 P2 FRIC2 4 0FRI 0T20 0T21
00400 FR3 P2 FRIC3 4 0FRI 0T23 0T24
00410 DUM8 P2 0 0 0 0
00420 DUM9 P2 0 0 0 0
00430 FR1SS P3 FRIC1 4 0FRI 0FR1 1FR1 1FR2
00440 FR2SS P3 FRIC2 4 0FRI 0FR2 1FR2 2FR2
00450 FR3SS P3 FRIC3 4 0FRI 0FR3 1FR3 2FR3
00460 DUM10 P2 0 0 0 0
00470 DUM11 P2 0 0 0 0
00480 FRVS Q3 EEAEE I 4 0VOW 0FRNSS 0VOC1 0VOC2
00490 MIVS Q3 ASAAARA 4 0VOW 0MIDSS 0VOC1 0VOC2
00500 BCVS Q3 OOU AWO 4 0VOW 0BCKSS 0VOC1 0VOC2
00502
00504
00506 (13) (cont.)
00508
00010 FRES Q3 H S F SH 4 0FRI 0FR1SS 0FR2SS 0FR3SS
00020 BRSTS Q3 PBTBKBNU 3 0BUR 0FR1SS 0FR2SS 0FR3SS
00030 STOPS Q3 VSSINUNU 3 0STO 0AVE 0F1 0A1
00040 DUM12 P2 0 0 0 0
00050 DUM13 P2 0 0 0 0
00060 NASA1 P2 NASGLI 14 0VO 0A1 0AVE
00070 NASGLI P2 NASGLI 14 0VO 0NASA1 0LPE
00080 DUM14 P2 0 0 0 0
00090 T51 P3 NAS1 3 0NAS 0F1 0F2 0F3
00100 T61 P3 NAS1 3 0NAS 0A1 0A2 0A3
00110 T71 P3 NAS1 3 0NAS 0NP 0NZ 0NZA
00120 T91 P3 NAS2 3 0NAS 0A1 0A2 0A3
00130 T81 P3 NAS2 3 0NAS 0F1 0F2 0F3
00140 T101 P3 NAS2 3 0NAS 0NP 0NZ 0NZA
00150 T52 P3 GLI1 3 0NAS 0F1 0F2 0F3
00160 T62 P3 GLI1 3 0NAS 0A1 0A2 0A3
00170 T112 P3 GLI2 3 0NAS 0F1 0F2 0F3
00180 T122 P3 GLI2 3 0NAS 0A1 0A2 0A3
00190 DUM15 P2 0 0 0 0
00200 NAS1 P3 NAS1 3 0NAS 0T51 0T61 0T71
00210 NAS2 P3 NAS2 3 0NAS 0T81 0T91 0T101
00220 DUM16 P2 0 0 0 0
00230 GLI1 P3 GLI1 3 0NAS 0T52 0T62 0NAS1
00240 GLI2 P3 GLI2 3 0NAS 0T112 0T122 0NAS2
00250 NAS1SS P2 NAS1 3 0NAS 0NAS1 1NAS1
00260 NAS2SS P2 NAS2 3 0NAS 0NAS2 1NAS2
00270 DUM17 P2 0 0 0 0
00280 NASALS Q3 M N NGNU 3 0NAS 0NP 0NAS1S 0NAS2S
00290 GLIDES Q3 W R L Y 3 0NAS 0F2 0GLI1 0GLI2
00300 DUM18 P2 0 0 0 0
00310 VOFR P2 VOIFRI 0 0LPE 0VOICE 0FRIC
00320
00330
00340 Table 3. The Current Signature Tables.
00350
00360
00370
00380
00390
00400
00410
00420
00430
00440
00450
00460
00470
00480
00490
00500
00502
00504
00506 (14)
00508
00010 The following counters exist
00020
00030 Name Input Level Hysteresis
00040 VOI-C 0VOICE 5 2
00050 FRI-C 0FRIC 4 3
00060 VOWEL 0VOWEL 4 2
00070 BURST 0BURST 3 3
00080 STOP 0STOP 4 3
00090 NASGLI 0NASGL 4 3
00100 EE Q1FRVS 3 3
00110 AE Q2FRVS 3 3
00120 E Q3FRVS 2 3
00130 I Q4FRVS 2 3
00140 AS Q1MIVS 2 3
00150 AA Q2MIVS 2 3
00160 AR Q3MIVS 2 3
00170 A Q4MIVS 3 3
00180 OO Q1BCVS 4 3
00190 U Q2BCVS 2 3
00200 AW Q3BCVS 2 3
00210 O Q4BCVS 4 3
00220 M Q1NASA 4 3
00230 N Q2NASA 4 3
00240 NG Q3NASA 2 3
00250 S Q2FRES 3 3
00260 F Q3FRES 4 3
00270 PB Q1BRST 3 3
00280 TB Q2BRST 4 3
00290 KB Q3BRST 2 3
00300 VS Q1STOP 2 3
00310 SI Q2STOP 4 3
00320 W Q1GLID 4 2
00330 R Q2GLID 3 3
00340 L Q3GLID 2 3
00350 Y Q4GLID 2 3
00360 VZ 0VOFR 2 3
00370
00380
00390 Table 5. List of Counters
00400
00410
00420
00430
00440
00450
00460
00470
00480
00490
00500
00502
00504
00506 (15)
00508