METHOD FOR ERROR RECOVERY IN METHOD AND DEVICE FOR RECOGNISING A USER PRESENTATION THROUGH ASSESSING THE RELIABILITY OF A LIMITED SET OF HYPOTHESES

Abstract: For recognizing a user presentation that is represented as a plurality of physical components through assessing a feasibility of a provisional recognition, various presentation components thereof are recognized in successive steps. In particular, along with the successive steps of the recognizing, a limited set of hypotheses is built for the presentation. At predetermined instants along the steps, these hypotheses are matched with an associated reliability level. Upon non-meeting of the reliability level with respect to one or more components as signalling an impending dead-end situation, the feasibility is improved by for the latter such component driving the method to an error recovery procedure to improve the reliability of one or more elements of the limited set.

Description: METHOD FOR ERROR RECOVERY IN METHOD AND DEVICE FOR RECOGNISING A USER PRESENTATION THROUGH ASSESSING THE RELIABILITY OF A LIMITED SET OF HYPOTHESES BACKGROUND OF THE INVENTION The invention relates to a method as recited in the preamble of Claim 1. A pπme application of the method is for recognizing speech that can be a basis for searching in vaπous static or dynamic environments, such as telephone directories, tra tables, and other. Generally, a human user will need an entry deπved from a data base and defined through a stπng of request components that must be successively recognized. A pπme difficulty is that for one or more of these components the spectrum of choice may be orders of magnitude larger than for other components. For example, in a train time table environment there may be several thousands of destination stations, but the departure time can usually be expressed in only a few tens of items, such as the ten digits and combinations thereof, and furthermore certain terms like noon, early, before, and a few others Other types of databases feature similarly varying levels of search diversity. The inventors have appreciated that the different levels of complexity among the vaπous subtasks associated to recognizing the respective speech items warrant a higher level of organization than recognizing on the level of the respective single words or request components alone In particular, if the recognizing process has gotten or threatens to get into a dead-end situation, remedy therefor should be undertaken as early as reasonable Such dead-end situation could occur if the incomplete recognition process still has many possible outcomes, but none of these outcomes can be assessed as representing a possibly correct one- the arrangement of outcomes would thereby be unfeasible The method may in similar manner be used for recognizing vaπous other types of user presentation that are represented as a plurality of components, such as a facial image of the user that may be accompanied by a bar-code or another type of code entry, and wherein the face must be matched to a photograph or a set of photographs that is stored m . The error remedy may again be vaπous, such as scanning the image with another resolution or another colour filter A still further type of presentation may be a combination of image and speech SUMMARY TO THE INVENTION In consequence, amongst other things, it is an object of the present invention to let the procedure detect the evolving absence of any way that could lead to success, and upon such detecting, transfer early to an error recovery procedure. Now therefore, according to one of its aspects, the invention is characteπzed according to the characteπzmg part of Claim 1. Such error recovery may be on the level of a single word or component, such as having the user person repeat or spell it. On a higher level, it may be that the system poses an inquisitive or exhortatory statement to elicit from the user an utterance that would be better adapted to the actual recognition facilities, for example, the user could then choose another formulation for the request component in question. The invention differs from procedures that test the reliable recognition of only the most recent component, but instead looks to an actually reliable recognition of a combination of components. The invention also relates to a device being arranged to implement a method as recited supra. Further advantageous aspects of the invention are recited in dependent Claims. BRIEF DESCRIPTION OF THE DRAWING These and further aspects and advantages of the invention will be discussed more in detail hereinafter with reference to the disclosure of preferred embodiments, and in particular with reference to the appended Figures that show. Figure 1, an exemplary system architecture; Figure 2, a letter graph for recognizing the word "Miller", Figure 3, a flow chart of the procedure of the invention. DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS Figure 1 shows an exemplary system architecture with an interface 20 to a telephone system not shown. The system compπses network interface and I/O control unit 22, speech recognizer 24, a special spelling filter or postprocessor 26, speech understanding component 28, speech output unit 30, and data base 32 Further subsystems are: knowledge processing module 33, dialog control module 35, reliability assessment module 29, and error recovery module 31. In the architecture, most modules will read immediately from database 32 to avoid unnecessary copying of large data sets; by themselves, most blocks may correspond to elements known from other systems The gist of the invention is in particular centered in blocks 29, 31 that control the overall policy Spelling filter 26 should output one or more sequences of exclusively words with associated probability scores Subsequent to spelling filter 26, the recognizing of single words is finished. For in particular implementing the invention, block 29 assesses actual reliability If an unreliability is detected, an activation signal is sent to block 31 for effecting error recovery. This block decides in particular, which utterances have to be recovered Next, dialog control block 35 decides how the error recovery will have to proceed exactly For example, the dialog control 28 may ask the user for to repeat a particular request component, to spell it, or the recovery can be by steeπng a user person to provide alternative information The error can be recovered by combining with earlier data that had been deπved by blocks 24, 26 from components already received before As the. interface between speech recognition, spelling, and speech understanding, often word graphs are used A word graph is a compact representation of vaπous plausible sentence or sequence hypotheses. Every individual path through the graph is a hypothesis At every dialog turn, the speech recognizer processes the caller's utterance and produces a word graph In the spelling mode, a detailed graph is used that consists of letters and qualifiers, the latter being used for giving extra information on a particular letter, such as the term "double", "accented" The spelling mode may be controlled either by the system or by the user person Otherwise, the recognizer is configured to recognize single-word utterances only, so that a word graph then represents one or more candidate stπngs built from single words To achieve a better recognition score and real-time operation, the recognizer's vocabulary may switched-over to enable only those words expected in the actual dialog state In this respect, Figure 2 gives a letter graph for recognizing the word "Miller" The links between vaπous successive states may get scores for recognizing the letters as shown, wherein the third letter may be recognized as a straight alphabetic element ("L"), or an element of an international spelling alphabet, of which the representations for "T" and "H" have been shown Finally, a qualifier "double" can be used to indicate that the next letter "L" occurs twice Similar graphs apply to word sequences in a sentence Hereinafter, vaπous concrete recognition procedures will be discussed The hypotheses used for each component thereof carry an indication as to the partial reliability of the associated matching of the component in question with the example In particular, a score item is formed as the negative log of the a posteriory probability that is generated directly by the recognizer proper As exemplary vehicle, an "N-best list" may be used as an alternative to a word-graph Hereinafter, the feasibility is determined for the "set of the best hypotheses", built from the applicable vehicle such as the exemplary N-best list or Word-graph, supra, elements of which which set are selected from all extant hypotheses In consequence, there is a two-tier selection process, the "set of the best" being selected in the second stage Now, the elements of an N-best list are {(hι,bι) (hr>j,bN)}, wherein the hypothesized recognition results are h„ each associated to a respective score b,, the lowest score representing the highest probability The list elements can be ordered according to increasing scores, and the number N need not be uniform over the recognition procedure Furthermore by way of example, the "set of the best " may consist of only the first F hypotheses, such as according to {(hι,bι),(h 2 ,b 2 ),(h 3 ,b 3 )}, wherein F=3. In the following, the N-best list is assumed present and the "set of the best" is assumed to consist of the first F≤N entπes thereof. The setting of F will be treated hereinafter. VARIOUS RELIABILITY MEASURES A. Vaπous reliability measures R may be used, of which a first one is the renormahzed probability of the "set of the best": In the case of the above scores this is simply: f(x)=exp(-ax), wherein for the general case the value of a may differ from 1. The "set of the best" may be defined according to one of the following procedures: 1. Empiπcally setting a uniform single value for F, 2. Empiπcally setting a score difference D s , all pairs (h„b,) with (b,-bι)<D s being in the set, B. A next possible reliability measure R=(l-F/N), that is the "relative fraction of the hypotheses not in the "set of the best", which measure is generally combined with the score-distance cπteπum (2). C A next possible reliability measure R is the absolute number of hypotheses in the "set of the best", generally combined with the score-distance cπteπum. D A next possible reliability measure R is the differential score of the first element not in the "set of the best": R=(bF + ι-bτ). Here, the "set of the best" may be determined by the straight setting of F, or by the above renormahzed probability (1) After setting an empiπcal accumulated probability, 0<P C <1, F is given by the relations P F <P C , but P F+I ≥P C Herein, P F is the above renormahzed probability of the "best set" with exactly F elements, as given by expression (1). All descπbed reliability measures have as a consequence for their use, that the feasibility of the set of hypotheses for the combination has a higher probability for recognition if the elements of the set have a higher reliability. The actual level of the reliability may tπgger vaπous system responses. In particular, straight thresholding may be applied. That is, if actual reliability is lower, the hypothesis will be discarded as useless, and a special error recovery procedure will be initiated. For example, a user person will be asked to repeat an information that had already been earlier requested by the system Otherwise, the hypothesis in question will be considered useless. The threshold may be set at different levels that are appropriate for the actually present data. A particular application for the reliability indicator is to calculate it separately for the hypothesis set of each respective user indication. The feasibility of each of these hypothesis sets will be calculated separately, such as according to the above threshold method. TWO VARIANTS FOR SIMULTANEOUS USAGE OF PLURAL SETS OF HYPOTHESES A. In the first place, the various sets of hypotheses for the individual user indications will be combined to produce a new set for the combined indications. In case of a personal name consisting of given name and family name, the N-best list of the complete name will appear, such as {"Christoph Krarnm" and "Alexander Klamm" }, these two family names being particularly difficult to be discriminated from each other. Often, such family name is the first item the recognition system will look to. The discussed procedure can be applied on the combined set of hypotheses in similar manner as described, and therefore will contribute to its reliability. If the set is unfeasible for use, this means that at least one of the respective single serialized hypotheses was not useful. Then, and possibly subject to assessing of the various reliabilities of the respective single hypotheses, an appropriate action may be started. B. In the scenario of the so-called hierarchical recognition, often only the first recognition step is critical. For example, in searching for the correct telephone extension number, the system must recognize the spelling of a single name out of a set of many personal names, in certain cases up to 200,000 or even more. Subsequent recognition steps will often have lexica with no more than in the order of ten entries. If now the procedure according to the invention is applied on the sets of hypotheses pertaining to these latter non-critical recognition steps, their lack of feasibility will lead to consider the earlier hypotheses that pertain to the critical step as being useless from the start. This procedure can therefore be only combined with the immediate reliability of the first recognition step. If the latter one were nevertheless of high reliability, this leads immediately to concluding that the later step is itself unreliable, which incites to take appropriate measures with respect to this later step. FURTHER PROCEDURES FOR CALCULATING RELIABILITY CRITERIA In addition to the above discussed procedure, all other standard calculating methods for single reliabilities may be used for the new task. The simplest manner to do so is to use these methods for calculating the (single) reliability levels of the best hypothesis hi and to use this as a general indication for the overall reliability of the whole set of hypotheses. Various ones of the procedures may be used also in corresponding manner for "set of the best", m corresponding manner as has been done here for the renormahzed probability, which had already been used by introducing as a single reliability as descπbed in German Patent Application 19740147.3, corresponding US Application Seπal No 08/...,..., PHD97127, to the present assignee, and herein incorporated by reference. Thereamong, several cπteπa can provide indications on the recognizing conditions, even independently from the acquired set of hypotheses, and therefore also independently from the respective single assessments therein. For example, the adaptation quality of noise interference models may indicate whether a speech recognition system is being used in a noisy environment, which offers an actual adversity that leads to recognition difficulties Other such adverse environments can be due to very loud or very soft speaking, etcetera. Countermeasures will also provide information for the use as discussed in hierarchical recognizing, because they can detect problems with subsequent non-cπtical recognition steps that follow a useful first recognition step Figure ^ is a flow chart of the procedure of the invention. In block 40, the procedure is started with assignment of the necessary hardware and software facilities. In block 42 the system detects whether for the actual user requirement the procedure has effectively become complete If yes, m block 56 the procedure is terminated with effecting the appropπate action viz a viz a user, such as by outputting the required information in speech or hardcopy, or by dπving a service authoπty into rendeπng the appropπate service Otherwise, the system in block 44 receives the next element of the user utterance or utterances If appropπate, this may be effected in a dialog organization The system-outputted speech has been omitted for brevity In block 46, the next element, is processed by the system for recognition, and if appropπate, also for its understanding As recited earlier, this will produce a set of hypotheses for the element in question. In block 48, the hypotheses are joined with such earlier hypotheses as remaining "in the race" from the recognition process as executed up to then, so that a set of joined hypotheses are formed, each having its own associated reliability quantity In block 50, the set of hypotheses now formed is assessed with respect to an outlook for possibly successful recognition or otherwise, with respect to occurrence of a dead-end situation If no such dead-end situation is expected, the system returns to block 42 If however, the outlook for success is bleak, the system proceeds to block 52, wherein one of the partial hypotheses now present is selected for necessary improvement, and in block 54, corrective action is being undertaken Subsequently, the upgraded hypothesis from the corrective action is fed back into block 48 The above upgrading may again be effected in a dialog structure, and if the case be, effectively in a smaller version of the overall Figure as shown, which for brevity has been symbolized by the block 54 only.

Claims: CLAIMS: 1. A method for recognizing a user presentation that is represented as a plurality of physical components through assessing a feasibility of a provisional recognition, by in successive steps recognizing various presentation components thereof, characterized in that along with the successive steps of said recognizing a limited set of hypotheses is built as regarding said presentation, which hypotheses pertain to a combination of components, and which hypotheses at predetermined instants along said steps are matched with an associated reliability level, and upon non-meeting of said reliability level with respect to one or more said components as signalling an impending dead-end situation, improving such feasibility through for the latter such one or more components driving the method to an error recovery procedure to improve the reliability of one or more elements of said limited set. 2. A method as claimed in Claim 1, wherein said user presentation represents a user request that is received in the form of speech. 3. A method as claimed in Claim 1, wherein such reliability level is co-determined by one or more interference parameters that indicate an actual adversity for such recognizing. 4. A method as claimed in Claim 1, wherein said reliability level is assessed for a single one of said hypotheses. 5. A method as claimed in Claim 1, wherein said reliability is based on a renormahzed probability of the "set of the best". 6. A method as claimed in Claim 1, wherein said reliability is based upon restricting said "set of the best" to a predetermined number of such hypotheses. 7. A method as claimed in Claim 1, wherein said reliability is based upon restricting said "set of the best" to hypotheses that have at most a predetermined differential score with respect to an actually most reliable one of said hypotheses. 8. A device being arranged to implement as method as recited in Claim 1, said device having recognizing means for receiving and recognizing a user presentation that is represented as a plurality of physical components through assessing a feasibility of a provisional recognition, by in successive steps recognizing various presentation components, characterized by comprising hypothesis building means fed by said recognizing means for along with the successive steps of said recognizing building a limited set of hypothesis as regarding said presentation, which hypothesis pertain to a combination of components, and which hypotheses at predetermined points along said steps are matched with an associated reliability level, and having drive signaling output means fed to said recognizing means for upon non-meeting of said reliability level with respect to one or more said components as signaling an impending dead-end situation, improving such feasibility through for the latter such one or more components driving the recognizing means to an error recovery procedure to improve the reliability of one or more elements of said limited set.

Inventors: KELLNER ANDREAS, RUEBER BERNHARD J, SCHRAMM HAUKE, KELLNER, ANDREAS, RUEBER, BERNHARD, J., SCHRAMM, HAUKE
Applicants: KELLNER ANDREAS, KONINKL PHILIPS ELECTRONICS NV, PHILIPS CORP INTELLECTUAL PTY, RUEBER BERNHARD J, SCHRAMM HAUKE, KELLNER, ANDREAS, KONINKLIJKE PHILIPS ELECTRONICS N.V., PHILIPS CORPORATE INTELLECTUAL PROPERTY GMBH, RUEBER, BERNHARD, J., SCHRAMM, HAUKE
Application year: 1999
Main classification: G10L15
IPC codes: G06F3, G10L15