Abstract:
A signal processing arrangement for discriminating a time varying band-limited input signal from other signals using time encoded signals. A received input signal is encoded as a time encoded signal symbol stream from which a fixed size matrix is derived. A plurality of archetype matrices corresponding to a plurality of different input signals are stored, each having been generated by encoding a corresponding input signal into a respective time encoded signal stream from which a respective archetype matrix is derived. A plurality of features are selected and excluded from the archetype matrices to generate corresponding archetype exclusion matrices. An input signal exclusion matrix is generated from the input signal matrix and each of the archetype exclusion matrices. The input signal exclusion matrix is compared with each of the archetype exclusion matrices to generate an output identifying the input signal.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to signal processing arrangements, and more particularly to such arrangements which are adapted for use with time varying band-limited input signals, such as speech. 
     2. Description of the Related Art 
     For a number of years the time encoding of speech and other time varying band-limited signals has been known, as a means for the economical coding of time varying signals into a plurality of Time Encoded Speech or Signal (TES) descriptors or symbols to afford a TES symbol stream, and for forming such a symbol stream into fixed dimensional, fixed size data matrices, where the dimensionality and size of the matrix is fixed, a priori, by design, irrespective of the duration of the input speech or other event to be recognized. See, for example: 
     1. U.K. Patent No. 2145864 and corresponding European Patent No. 0141497. 
     2. Article by J. Holbeche, R. D. Hughes, and R. A. King, &#34;Time Encoded Speech (TES) descriptors as a symbol feature set for voice recognition systems&#34;, published in IEE Int. Conf. Speech Input/Output; Techniques and Applications, pages 310-315, London, March 1986. 
     3. Article by Martin George &#34;A New Approach to Speaker Verification&#34;, published in &#34;VOICE +&#34;, October 1995, Vol. 2, No. 8. 
     4. U.K. Patent No. 2268609 and corresponding International Application No. PCT/GB92/00285 (WO92/00285). 
     5. Article by Martin George &#34;Time for TESPAR&#34; published in &#34;CONDITION MONITOR&#34;, September 1995, No. 105. 
     The time encoding of speech and other signals described in the above references have, for convenience, been referred to as TESPAR coding, where TESPAR stands for Time Encoded Signal Processing and Recognition. 
     It should be appreciated that references in this document to Time Encoded Speech, or Time Encoded Signals, or TES, are intended to indicate solely, the concepts and processes of time encoding, set out in the aforesaid references and not to any other processes. 
     In U.K. Patent No. 2145864 and in some of the other references already referred to, it is described in detail how a speech waveform, which may typically be an individual word or a group of words, may be coded using time encoded speech (TES) coding, in the form of a stream of TES symbols, and also how the symbol stream may be coded in the form of, for example, an &#34;A&#34; matrix, which is of fixed size regardless of the length of the speech waveform. 
     As has already been mentioned and as is described in others of the references referred to, it has been appreciated that the principle of TES coding is applicable to any time varying band-limited signal ranging from seismic signals with frequencies and bandwidths of fractions of a Hertz, to radio frequency signals in the gigaHertz region and beyond. One particularly important application is in the evaluation of acoustic and vibrational emissions from rotating machinery. 
     In the references referred to it has been shown that time varying input signals may be represented in TESPAR matrix form where the matrix may typically be one dimensional or two dimensional. For the purposes of this disclosure two dimensional or &#34;A&#34; matrices will be used but the processes are identical with &#34;N&#34; dimensional matrices where &#34;N&#34; may be any number greater than 1, and typically between 1 and 3. It has also been shown how numbers of &#34;A&#34; matrices purporting to represent a particular word, or person, or condition, may be grouped together simply to form archetypes, that is to say archetype matrices, such that those events which are consistent in the set are enhanced and those which are inconsistent and variable, are reduced in significance. It is then possible to compare an &#34;A&#34; matrix derived from an input signal being investigated with the archetype matrices in order to provide an indication of the identification or verification of the input signal. In this respect see U.K. Patent No. 2268609 (Reference 4) in which the comparison of the input matrix with the archetype matrices is carried out using fast artificial neural networks (FANN&#39;s). It will be appreciated, as is explained in the prior art, for time varying waveforms especially, this process is several orders of magnitude simpler and more effective than similar processes deployed utilizing conventional procedures and frequency domain data sets. 
     It has now been appreciated that the performance of TESPAR and TESPAR/FANN recognition and classification and discrimination systems can, nevertheless, be further significantly improved. 
     SUMMARY OF THE INVENTION 
     According to the present invention there is provided a signal processing arrangement for a time varying band-limited input signal, comprising coding means operable on said input signal for affording a time encoded signal symbol stream, means operable on said symbol stream for deriving a fixed size matrix indicative of said input signal, means for storing a plurality of archetype matrices corresponding to different input signals to be processed, each of said archetype matrices being afforded by coding a corresponding one of said different input signals into a respective time encoded signal symbol stream and coding each said respective symbol stream into a respective archetype matrix, means operable on all said archetype matrices for selecting a plurality of features thereof, means operable on each of said archetype matrices for excluding from them said selected features to afford corresponding archetype exclusion matrices, means operable on said input signal matrix and on each of said exclusion matrices to afford an input signal exclusion matrix, and means for comparing the input signal exclusion matrix with each of the archetype exclusion matrices for affording an output indicative of said input signal. 
     In one arrangement for carrying out the invention it is arranged that said means operable on each of said archetype matrices is effective for excluding from them features thereof which are substantially common to afford said corresponding exclusion matrices. 
     In another arrangement for carrying out the invention it is arranged that said means operable on each of said archetype matrices is effective for excluding from them features thereof which are not similar to afford said corresponding exclusion matrices. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     An exemplary embodiment of the invention will now be described, reference being made to the accompanying drawings, in which: 
     FIG. 1, is a pictorial view of a full event archetype matrix for the digit &#34;Six&#34;; 
     FIG. 2, is a table depicting in digital terms the matrix of FIG. 1; 
     FIG. 3, is a pictorial view of a full event archetype matrix for the digit &#34;Seven&#34;; 
     FIG. 4, is a table depicting in digital terms the matrix of FIG. 3; 
     FIG. 5, is a pictorial view of a top 60 event archetype matrix for the digit &#34;Six&#34;; 
     FIG. 6, is a table depicting in digital terms the matrix of FIG. 5; 
     FIG. 7, is a pictorial view of a top 60 event archetype matrix for the digit &#34;Seven&#34;; 
     FIG. 8, is a table depicting in digital terms the matrix of FIG. 7; 
     FIG. 9, is a block schematic diagram of an exclusion archetype construction in accordance with the present invention; 
     FIGS. 10a, 10b and 10c (FIGS. 10b and 10c having a reduced scale) when laid side-by-side constitute a bar graph depicting the common events of the digit &#34;six&#34;; 
     FIGS. 11a, 11b and 11c (FIGS. 11b and 11c having a reduced scale) when laid side-by-side constitute a bar graph depicting the common events of the digit &#34;Seven&#34;; 
     FIGS. 12a, 12b and 12c (FIGS. 12b and 12c having a reduced scale) when laid side-by-side constitute a bar graph corresponding to that of FIGS. 10a, 10b and 10c in which the events are ranked; 
     FIGS. 13a, 13b and 13c (FIGS. 13b and 13c having a reduced scale) when laid side-by-side constitute a bar graph corresponding to that of FIGS. 11a, 11b and 11c in which the events are ranked; 
     FIG. 14, is a bar graph depicting similar events of the digit &#34;Six&#34; ranked in magnitude (window size=5); 
     FIG. 15, is a bar graph depicting similar events of the digit &#34;Seven&#34; ranked in magnitude (window size=5); 
     FIG. 16, is a bar graph depicting similar events of the digit &#34;Six&#34; ranked in magnitude (window size=10); 
     FIG. 17, is a bar graph depicting similar events of the digit &#34;Seven&#34; ranked in magnitude (window size=10); 
     FIG. 18, is a pictorial view of a top 60 event exclusion archetype matrix for the digit &#34;Six&#34; (window size=5); 
     FIG. 19, is a table depicting in digital terms the matrix of FIG. 18; 
     FIG. 20, is a pictorial view of a top 60 event exclusion archetype matrix for the digit &#34;Seven&#34; (window size=5); 
     FIG. 21, is a table depicting in digital terms the matrix of FIG. 20; 
     FIG. 22, is a pictorial view of the &#34;similar events&#34; excluded from the archetype matrix for the digit &#34;Six&#34; (window size=5); 
     FIG. 23, is a table depicting in digital terms the matrix of FIG. 22; 
     FIG. 24, is a pictorial view of a top 60 event exclusion archetype matrix for the digit &#34;Seven&#34; (window size=5); 
     FIG. 25, is a table depicting in digital terms the matrix of FIG. 24; 
     FIG. 26, is a pictorial view of a top 60 event exclusion archetype matrix for the digit &#34;Six&#34; (window size=10); 
     FIG. 27, is a table depicting in digital terms the matrix of FIG. 26; 
     FIG. 28, is a pictorial view of a top 60 event exclusion archetype matrix for the digit &#34;Seven&#34; (window size=10); 
     FIG. 29, is a table depicting in digital terms the matrix of FIG. 28; 
     FIG. 30, is a pictorial view of the &#34;similar events&#34; excluded from the archetype matrix for the digit &#34;Six&#34; (window size=10); 
     FIG. 31, is a table depicting in digital terms the matrix of FIG. 30; 
     FIG. 32, is a pictorial view of the &#34;similar events&#34; excluded from the archetype matrix for the digit &#34;Seven&#34; (window size=10); 
     FIG. 33, is a table depicting in digital terms the matrix of FIG. 32; and 
     FIG. 34, is a block schematic diagram of exclusion archetype interrogation architecture in accordance with the present invention. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     By way of example, the process in accordance with the invention will be described utilizing as an exemplar a system designed to recognize the digits 0-9 spoken by a single male individual. For simplicity the two acoustic utterances &#34;six&#34; and &#34;seven&#34; only, will be used to illustrate the process. 
     Referring to the drawings, FIG. 1 depicts an &#34;A&#34; matrix archetype constructed from 10 utterances of the word &#34;six&#34; spoken by a male speaker. This is what is called a full event archetype matrix because all the events generated in the TESPAR coding process are included in the matrix. 
     For clarity, FIG. 1 shows the distribution of TESPAR events in pictorial form. For numerical accuracy, FIG. 2 shows this distribution as events on a 29 by 29 table. 
     FIG. 3 depicts a similar full event archetype matrix created by the same male speaker for the digit &#34;seven&#34;, and FIG. 4 shows the distribution of events on a 29 by 29 table. 
     From the matrices of FIGS. 1 and 3 it can be seen that both matrices have a relatively large peak in the short symbol area (left hand corner) and a set of relatively small peaks, distributed away from this area. 
     It will be appreciated by those skilled in the art that this distribution of symbols is due to the fact that the words &#34;six&#34; and &#34;seven&#34; both contain preponderance of the &#34;S&#34; sibilant sound which produces many short (high frequency) &#34;epochs&#34; and hence many such symbols, relative to the rest of the &#34;voiced&#34; portion of the word. It would also be appreciated by those skilled in the art that the sibilant feature of the words &#34;six&#34; and &#34;seven&#34; is substantially common to both matrices and therefore provides little information regarding the difference between the two words. 
     The previous literature on TESPAR indicates that for most discriminative comparisons, all the events in the archetype need not be used and that it is commonly known that the top, say, 60 events from each of the archetypes can form an effective descriptive pattern for subsequent classification. FIGS. 5 and 6, and 7 and 8, show the distribution in the matrices of the top 60 events for the words &#34;six&#34; and &#34;seven&#34;. 
     It has been discovered that since the archetype to some extent represents the characteristic features of all the individual acoustic tokens which were used to construct it, then comparisons of these archetypes can enable both consistent similarities and consistent differences to be identified advantageously. For time varying signals such as speech, the TESPAR format uniquely enables such discriminations to be made. 
     It has now been discovered that the discriminations invoked by the means previously described in the literature, may be made significantly more efficient and effective and may thus more simply classify and separate acoustic and other vibrational events which will otherwise prove intractable. 
     In FIG. 9, the process is exemplified by means of what is here called &#34;exclusion archetypes&#34; or &#34;exclusion matrices&#34;. First the archetype matrices for the differing acoustic events are created from sets of acoustic input token &#34;A&#34; matrices. For the purpose of this illustration the archetype matrix of the word &#34;six&#34; (FIG. 1) will be compared with the archetype matrix of the word &#34;seven&#34; (FIG. 3). It will be seen from FIG. 9 that many (more than 2) archetypes may be compared by this means. The first step in the process is to identify those events which are common between archetype matrices for the digits &#34;six&#34; and &#34;seven&#34;. FIGS. 10a, 10b and 10c when laid side-by-side show the distribution of the common events in the archetype matrix of FIG. 1 for the digit &#34;six&#34; and FIGS. 11a, 11b and 11c when laid side-by-side show the distribution of the common events in the archetype matrix of FIG. 3 for the digit &#34;seven&#34;. This process identifies those matrix entries, which, because they are substantially identical, are less likely to contribute to the discriminative process between the (two) words. 
     If, however, these events although identical in their locations, were differently ranked in these common matrix locations, then they might still contribute significantly to a comparison using classical statistical correlation routines. Because of this, a second step is required in the process. 
     In this second step shown in FIG. 9, all the common (identical) events are ranked according to magnitude. It will be appreciated that rankings other than magnitude may be deployed to advantage in different circumstances but, for the purposes of this illustration, the events will be ranked on magnitude. The results of this process are shown in FIGS. 12a, 12b and 12c when laid side-by-side for the digit &#34;six&#34; and in FIGS. 13a, 13b and 13c when laid side-by-side for the digit &#34;seven&#34;. 
     Subsequent to the procedure illustrated in FIGS. 12a, 12b and 12c and in FIGS. 13a, 13b and 13c, the next step is to identify those events which are similarly ranked, based upon a set window size. If for example a window size of &#34;5&#34; were to be used, then five consecutive elements in the ranking are examined and those common events which fall within that window are included as &#34;similarly ranked&#34; events. This process proceeds starting with the highest events, with the window of &#34;5&#34; moving successfully from the highest events down to the lowest event. By this means common events which are similarly ranked based on a window size (of 5) are identified. 
     FIGS. 14 and 15 show the common events thus ranked based on a window size of &#34;5&#34; and FIGS. 16 and 17 for illustration show the common events of the same archetypes, ranked on a window size of &#34;10&#34;. 
     As a final examination, the sub-set common to both matrices is correlated by whatever statistical measure forms part of the system specification and if these numbers are highly correlated then, since they are common, similarly ranked and highly correlated, they will not contribute significantly to the discriminative process and indeed on many occasions will be the cause of misclassification. The following &#34;COMPARISON&#34; chart shows the correlation score for these &#34;common . . . etc . . . events&#34; based on a window size of both &#34;5&#34; and &#34;10&#34;. It will be seen that these events have a 99.36% correlation which indicates that they are very closely similar. 
     
         ______________________________________Comparison                   Score______________________________________Full Archetype &#34;6&#34; versus Full Archetype &#34;7&#34;                        0.9896Top 60 Event Archetype &#34;6&#34; versus Top 60 Event Archetype                        0.9898Top 60 Event Exclusion Archetype &#34;6&#34; versus Top 60 Event                        0.2614Exclusion Archetype &#34;7&#34; (Window Size = 10)Top 60 Event Exclusion Archetype &#34;6&#34; versus Top 60 Event                        0.3065Exclusion Archetype &#34;7&#34; (Window Size = 5)Similar Events Excluded from Archetype &#34;6&#34; versus Similar                        0.9936Events Excluded from Archetype &#34;7&#34; (Window Size = 10)Similar Events Excluded from Archetype &#34;6&#34; versus Similar                        0.9936Events Excluded from Archetype &#34;7&#34; (Window Size = 5)______________________________________ 
    
     The final step in creating the exclusion archetype matrices is to exclude the events thus identified from the archetype matrices concerned in this case from the archetype matrices for the digits &#34;six&#34; and &#34;seven&#34;. This then leaves in the matrices only those events which contribute significantly to the discrimination between the two words. 
     FIGS. 18 and 19 depict the top 60 event exclusion archetype matrix for the digit &#34;six&#34; with a window size of &#34;5&#34;. FIGS. 20 and 21 depict the top 60 event exclusion archetype matrix for the digit &#34;seven&#34; with a window size of &#34;5&#34;. From a comparison of the exclusion matrices of FIGS. 18 and 20, it can be seen that they are significantly different, and show substantially only those events which contribute significantly to the discrimination between the two words. For the sake of interest FIGS. 22 and 23 depict a matrix showing the &#34;similar events&#34; excluded from the archetype matrix for the digit &#34;six&#34;, with a window size of &#34;5&#34;, and FIGS. 24 and 25 depict a similar matrix showing the &#34;similar events&#34; excluded from the archetype matrix for the digit &#34;seven&#34;, with a window size of &#34;5&#34;. 
     FIGS. 26 to 33 correspond essentially to FIGS. 18 to 25 already referred to, except that they relate to a window size of &#34;10&#34; rather than &#34;5&#34;. 
     Having created the exclusion archetype matrices such as in FIGS. 18 and 20 and FIGS. 26 and 28, these are then used as the archetype matrices for comparison with input utterances as shown in FIG. 34. By this means a normal unmodified matrix derived from an input utterance, for example of the digit &#34;six&#34; or &#34;seven&#34; is sequentially processed performing a logical &#34;AND&#34; function of the input matrix with the exclusion archetypes 1 to N etc. The modified matrix so produced is then correlated with the exclusion archetype matrices created as described, in this case the archetype matrices of the digits &#34;six&#34; and &#34;seven&#34;. The correlation scores produced by this means are interrogated by some form of decision logic. In the case shown in FIG. 34, the &#34;highest score&#34; is selected as the winner. FIG. 34 thus shows the processing involved in decision making at interrogation. 
     To exemplify the practical advantages of the procedures described, the archetype matrices shown in previous diagrams have been used for comparison against 10 independent utterances of the word &#34;six&#34;, and 10 of the word &#34;seven&#34; spoken by the same male speaker who created the separately generated data for the archetypes. Complete full input matrices have been examined together with matrices limited to the top 60 events. The scores of individual utterances concerned are shown in the following tables: 
     
                       TABLE 1______________________________________Correlation Scores for Input Matrices versus Full Event ArchetypesInput Matrix       &#34;Six&#34;   &#34;Seven&#34;______________________________________Utterance 1 for &#34;Six&#34;              0.9569  0.9762Utterance 2 for &#34;Six&#34;              0.9882  0.9924Utterance 3 for &#34;Six&#34;              0.9955  0.9756Utterance 4 for &#34;Six&#34;              0.9802  0.9510Utterance 5 for &#34;Six&#34;              0.9826  0.9548Utterance 6 for &#34;Six&#34;              0.9565  0.9188Utterance 7 for &#34;Six&#34;              0.9675  0.9331Utterance 8 for &#34;Six&#34;              0.9914  0.9949Utterance 9 for &#34;Six&#34;              0.9935  0.9932Utterance 10 for &#34;Six&#34;              0.9693  0.9412Utterance 1 for &#34;Seven&#34;              0.9467  0.9759Utterance 2 for &#34;Seven&#34;              0.9806  0.9592Utterance 3 for &#34;Seven&#34;              0.9799  0.9662Utterance 4 for &#34;Seven&#34;              0.9118  0.9506Utterance 5 for &#34;Seven&#34;              0.9706  0.9894Utterance 6 for &#34;Seven&#34;              0.9804  0.9915Utterance 7 for &#34;Seven&#34;              0.9575  0.9809Utterance 8 for &#34;Seven&#34;              0.9805  0.9913Utterance 9 for &#34;Seven&#34;              0.9538  0.9786Utterance 10 for &#34;Seven&#34;              0.9691  0.9890______________________________________ 
    
     
                       TABLE 2______________________________________Correlation Scores for Input Matrices versus Top 60 Event ArchetypesInput Matrix       &#34;Six&#34;   &#34;Seven&#34;______________________________________Utterance 1 for &#34;Six&#34;              0.9569  0.9766Utterance 2 for &#34;Six&#34;              0.9881  0.9926Utterance 3 for &#34;Six&#34;              0.9954  0.9757Utterance 4 for &#34;Six&#34;              0.9801  0.9513Utterance 5 for &#34;Six&#34;              0.9825  0.9549Utterance 6 for &#34;Six&#34;              0.9564  0.9190Utterance 7 for &#34;Six&#34;              0.9674  0.9332Utterance 8 for &#34;Six&#34;              0.9914  0.9952Utterance 9 for &#34;Six&#34;              0.9935  0.9937Utterance 10 for &#34;Six&#34;              0.9692  0.9415Utterance 1 for &#34;Seven&#34;              0.9465  0.9755Utterance 2 for &#34;Seven&#34;              0.9804  0.9583Utterance 3 for &#34;Seven&#34;              0.9796  0.9653Utterance 4 for &#34;Seven&#34;              0.9115  0.9497Utterance 5 for &#34;Seven&#34;              0.9702  0.9880Utterance 6 for &#34;Seven&#34;              0.9802  0.9909Utterance 7 for &#34;Seven&#34;              0.9572  0.9803Utterance 8 for &#34;Seven&#34;              0.9802  0.9910Utterance 9 for &#34;Seven&#34;              0.9535  0.9779Utterance 10 for &#34;Seven&#34;              0.9689  0.9888______________________________________ 
    
     In these diagrams the decision and classification scores are shown in bold type. From this it may be seen that, without the special procedures herein described, the scores between the words &#34;six&#34; and &#34;seven&#34; are very close together indeed and that the normal procedure, using unmodified archetypes has produced a significant number of errors. Thus, for the unmodified full event archetype matrices shown in Table 1, utterances &#34;1&#34; and &#34;2&#34; and &#34;8&#34; of the word &#34;six&#34; are misclassified as &#34;seven&#34; and utterances &#34;2&#34; and &#34;3&#34; of the word &#34;seven&#34; are misclassified as &#34;six&#34;. For those matrices which include only the top 60 events as shown in Table 2, utterances &#34;1&#34;, &#34;2&#34;, &#34;8&#34; and &#34;9&#34; for the word &#34;six&#34; are misclassified as are utterances &#34;2&#34; and &#34;3&#34; for the word &#34;seven&#34;. 
     These results may be compared with those shown in Table 3 as follows where the routines described in the current disclosure have been deployed: 
     
                       TABLE 3______________________________________Correlation Scores for Masked Input Matrices versus Top 60 EventExclusion Archetypes (Window Size = 10)Input Matrix       &#34;Six&#34;   &#34;Seven&#34;______________________________________Utterance 1 for &#34;Six&#34;              0.8555  0.3387Utterance 2 for &#34;Six&#34;              0.8878  0.2833Utterance 3 for &#34;Six&#34;              0.8697  0.3178Utterance 4 for &#34;Six&#34;              0.9196  0.3445Utterance 5 for &#34;Six&#34;              0.9339  0.2506Utterance 6 for &#34;Six&#34;              0.8978  0.3032Utterance 7 for &#34;Six&#34;              0.7935  0.3085Utterance 8 for &#34;Six&#34;              0.9156  0.3502Utterance 9 for &#34;Six&#34;              0.8601  0.2172Utterance 10 for &#34;Six&#34;              0.8837  0.3310Utterance 1 for &#34;Seven&#34;              0.3526  0.6699Utterance 2 for &#34;Seven&#34;              0.6483  0.6812Utterance 3 for &#34;Seven&#34;              0.5031  0.8187Utterance 4 for &#34;Seven&#34;              0.3336  0.7784Utterance 5 for &#34;Seven&#34;              0.2517  0.7499Utterance 6 for &#34;Seven&#34;              0.6221  0.6915Utterance 7 for &#34;Seven&#34;              0.4005  0.7658Utterance 8 for &#34;Seven&#34;              0.4677  0.7084Utterance 9 for &#34;Seven&#34;              0.5854  0.6114Utterance 10 for &#34;Seven&#34;              0.4395  0.6493______________________________________ 
    
     From this it may be seen that using the procedures now disclosed the separations achieved are significantly greater than previously and, significantly, there are no misclassifications at all in this data. 
     As a further aid to understanding, the scoring system employed in the various examples which have been given is as follows: 
     A Separation Score has a valid Range of 0.00&lt;=Score&lt;=1.00 
     A Separation Score of 1.00 means the two matrices are Identical. 
     A Separation Score of 0.00 means the two matrices are Orthogonal. 
     One method of Separation Scoring is Correlation. 
     Also, the procedure used to calculate the correlation score between two TES matrices may typically be as follows: 
     Synopsis 
     s=score (x,y) 
     Description 
     s=score (x,y) returns the correlation score between the two matrices x and y, where x and y have the same dimensions. 
     A measure of similarity between an archetype and an utterance TES matrix, or between two utterance TES matrices is given by the correlation score. The score returned lies in the range from 0 indicating no correlation (orthogonality) to 1 indicating identity. 
     Example 
     score (a,a) 
     ans=1 
     score (a,abs(sign(a)-1)) 
     ans=0 
     Algorithm 
     If A and B are two matrices then their correlation score is calculated as follows: ##EQU1## 
     Note that for two vectors A and B their dot-product is 
     
         A·B=|A∥B|cos θ 
    
     where θ is the angle between the two vectors. 
     If we rearrange this we get ##EQU2## where 
     
         A·B=a.sub.1 b.sub.1 +a.sub.2 b.sub.2 + . . . +a.sub.n b.sub.n =Σab ##EQU3## 
    
     Thus if we treat an n-by-m matrix as a 1-by-nm vector then we see that ##EQU4## 
     The correlation score is therefore simply the square of the cosine of the angle between the two matrices A and B. 
     It will be obvious to those skilled in the art, that the procedures disclosed will be a very effective pre-processing strategy when applying TESPAR Matrices to Artificial Neural Networks (ANN&#39;s). 
     In the procedures which have been described the &#34;common events&#34; which occur in a signal matrix and in archetype matrices are &#34;excluded&#34; in order to help in input signal identification. 
     It should also be appreciated that similar principles may be used to cause &#34;non-common events&#34; rather than &#34;common events&#34; to be excluded, thereby enabling the &#34;common events&#34; derived from matrices which claim to be from the same source, e.g. the same speaker, to be compared, typically using ANN&#39;s, for signal verification and other purposes.