Learning intended user actions

A method and system are provided. The method includes receiving, by a microphone and camera, user utterances indicative of user commands and associated user gestures for the user utterances. The method further includes parsing, by a hardware-based recognizer, sample utterances and the user utterances into verb parts and noun parts. The method also includes recognizing, by a hardware-based recognizer, the user utterances and the associated user gestures based on the sample utterances and descriptions of associated supporting gestures for the sample utterances. The recognizing step includes comparing the verb parts and the noun parts from the user utterances individually and as pairs to the verb parts and the noun parts of the sample utterances. The method additionally includes selectively performing a given one of the user commands responsive to a recognition result.

BACKGROUND

Technical Field

The present invention relates generally to information processing and, in particular, to the fields of speech and gesture recognition.

Description of the Related Art

Work involving resolving anaphora (where, as used herein, anaphora refers to pronouns) in a multimodal environment is rule-based and does not employ learning. Accordingly, such prior art is therefore static and brittle. That is, such prior art is brittle in the sense that voice transcription applied to terse assertions is generally of very poor quality and, hence, rules that depend in part on accurate or near-accurate transcription of words can be failure prone. Thus, there is a need for a more dynamic and non-ruled based approach to multimodal command recognition capable of learning and resolving anaphora.

SUMMARY

According to an aspect of the present principles, a method is provided. The method includes receiving, by a microphone and camera, user utterances indicative of user commands and associated user gestures for the user utterances. The method further includes parsing, by a hardware-based recognizer, sample utterances and the user utterances into verb parts and noun parts. The method also includes recognizing, by a hardware-based recognizer, the user utterances and the associated user gestures based on the sample utterances and descriptions of associated supporting gestures for the sample utterances. The recognizing step includes comparing the verb parts and the noun parts from the user utterances individually and as pairs to the verb parts and the noun parts of the sample utterances. The method additionally includes selectively performing a given one of the user commands responsive to a recognition result.

According to another aspect of the present principles, a system is provided. The system includes a microphone and camera for receiving user utterances indicative of user commands and associated user gestures for the user utterances. The system further includes a hardware-based recognizer for parsing sample utterances and the user utterances into verb parts and noun parts, and recognizing the user utterances and the associated user gestures based on the sample utterances and descriptions of associated supporting gestures for the sample utterance by comparing the verb parts and the noun parts from the user utterances individually and as pairs to the verb parts and the noun parts of the sample utterances. The system also includes a user command selective execution device for selectively performing a given one of the user commands responsive to a recognition result.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present principles are directed to learning intended user actions. Embodiments of the present principles learn intended user actions utilizing speech and gesture recognition.

In an embodiment, the present principles involve interactive machine learning (also known in the literature as online machine learning) of multimodal commands. In an embodiment, the present principles can be implemented as a system or method for spoken language and gestural recognition. The user can make gestures and/or issue speech commands in front of a gesture recognition device to execute a command. At the trigger of the command by a user, the system and method can provide real time feedback. Each natural language command can be broken down into a verb and noun and the system can learn to associate the action with the verb and the noun separately in combination with the gesture or together combined with the gesture. Furthermore, the system and method can learn new ways of articulating and gesticulating intent though positive (i.e., user accepted) and negative (i.e., user rejected) examples.

Advantageously, the present principles can be applied to many situations not capable of being addressed in an acceptable manner, if at all, by the prior art. For example, in an embodiment, the present principles can overcome the following problem: a user intends that a desired action be carried out by a computer system and/or other processor-enabled device in response to using a combination of speech and gestural actions, but because of the very terse typical utterance and use of anaphora (pronouns), it is not easy for the computer system and/or device to accurately transcribe the spoken words and thereby understand the intended behavior of the user. Embodiments of the present principles can readily provide the computer system with the capability to process anaphora in such a situation. These and other advantages of the present principles as well as situations to which the present principles can be applied are readily determined by one of ordinary skill in the art, given the teachings of the present principles provided herein, while maintaining the spirit of the present principles.

Moreover, the present principles are advantageously adept at processing deixis (deixis herein refers to words and phrases that cannot be fully understood without additional spatial and/or contextual information). As such, phrases that can be difficult to understand without additional spatial and/or contextual information can be readily processed in accordance with the present principles.

Further, while one or more embodiments are directed to pointing as a gesture to which the present principles are applied, the present principles are not limited to solely pointing and, thus, other gestures (for example flicking, pushing or pulling gestures) can also be used in accordance with the teachings of the present principles, while maintaining the spirit of the present principles.

Moreover, it is to be appreciated that system200described below with respect toFIG. 2is a system for implementing respective embodiments of the present principles. Part or all of processing system100may be implemented in one or more of the elements of system200.

Further, it is to be appreciated that processing system100may perform at least part of the method described herein including, for example, at least part of method300ofFIGS. 3-4. Similarly, part or all of system200may be used to perform at least part of method300ofFIGS. 3-4.

FIG. 2shows an exemplary system200for learning intended user actions utilizing speech and gesture recognition, in accordance with an embodiment of the present principles. The system200includes a gesture recognition device210, a verb (action word) recognizer220, a noun and pronoun recognizer230, a part of speech to user gesture association device240, a feedback generator250, a confidence indication generator260, a user command selective execution device270, a memory device280, an utterance extender281, a probability of action determination device290, and an arbiter295. The gesture recognition device210, the verb recognizer220, the noun and pronoun recognizer230, the utterance extender281, the part of speech to user gesture association device240, and the memory device280form a user speech and associated gesture recognizer298. The user speech and associated gesture recognizer299, the feedback generator250, and the confidence indication generator260form a user command learning device299.

The gesture recognition device210recognizes actions and/or gestures (hereinafter “gestures”) performed by a user. In an embodiment, the gesture recognition device210includes a motion capture device211, a gesture database212, and a user gesture to stored gesture comparison device213. In an embodiment, the motion capture device211can include a camera, a camcorder, or any other type of image capture device. While described as a motion capture device, element211can be any type of device capable of capturing a user gesture, including static gestures where no or minimal movement is performed by the user. In an embodiment, the gesture database212stores expected user gestures and can be supplemented by new (e.g., previously unstored) gestures as they are captured and used by system200. The user gesture to stored gesture comparison device213compares contemporaneously performed user gestures to gestures in the gesture database212in order to recognize the contemporaneously performed user gestures. In an embodiment, the user gesture to stored gesture comparison device213can provide an output indicative of a particular recognized gesture (e.g., an output indicative of the user pointing and where the user is pointing [akin to a mouse pointer], and so forth).

The verb recognizer220recognizes verbs from utterances spoken by the user.

The noun and pronoun recognizer230recognizes nouns and pronouns from utterances spoken by a user.

The part of speech to user gesture association device240associates parts of speech (e.g., verbs, nouns, and pronouns) to user gestures.

The feedback manager250records and/or manages feedback (also referred to herein as evidence) relating to whether a pair of {action word, noun/pronoun} together with an associated gesture indicates that a user expects a particular system response and/or relating to whether any one of the {action word or noun/pronoun} together with an associated gesture indicates that a user expects a particular system response.

A confidence indication generator260generates confidence indications (which can take any form, including a score) relating to whether a pair of {action word, noun/pronoun} together with an associated gesture indicates that a user expects a particular system response and/or relating to whether any one of the {action word or noun/pronoun} together with an associated gesture indicates that a user expects a particular system response. In an embodiment, the confidence indication generator260can include an error indication generator261that represents a degree of error or uncertainty in the system's confidence that a pair of {action word, noun/pronoun} together with an associated gesture are indicative of a particular system response and/or a degree of error or uncertainly in the system's confidence that any of the {action word or noun/pronoun} together with an associated gesture are indicative of a particular system response. In an embodiment, one or more of the confidence indications and the error indications can be used. In an embodiment, one or both of the confidence indications and the error indications can be determined responsive to the feedback recorded and/or managed by the feedback manager250. In an embodiment, the confidence indications and/or the error indications can be generated responsive to a predetermined number of times that a pair of {action word, noun/pronoun} and/or any one of the {action word or noun/pronoun} are deemed to be successfully implemented (i.e., in response to the user's request to take an action, the system responds and the user does not immediately attempt to undo the action).

The user command selective execution device270selectively executes user requests for system response. The selectivity of execution of these system responses can depend on one or more criteria. Such criteria include, but are not limited to, evidence (e.g., supporting evidence, contrary evidence), user intent (e.g., as represented by affirmance/allowance of an action by a user or a request to undo an action by the user), and so forth. In an embodiment, the evidence can include feedback and/or confidence indications and/or error indications.

The memory device280stores evidence for particular combinations of action words potentially spoken in combination with a noun/pronoun in the presence of a recognized user gesture. In an embodiment, the memory device280stores “seed expressions” that may accompany a given gesture (e.g., the gestures stored in the gesture database212) that are indicative of particular, expected, system responses. These seed expressions can be used to build up an ever increasing lexicon through positive and negative examples as indicated through evidence (e.g., feedback, confidence indications, and/or error indications). In an embodiment, the memory device280can include a statistical knowledge repository283that keeps track of words and phrases used in conjunction with recognized gestures, and how often each of these lead to accepted system actions versus system actions which are rejected via an undo request.

The utterance extender281can extend sample utterances (e.g., seed expressions) prior to or in parallel with training. Such extensions can be based on synonyms, common known mistranscriptions of either the sample utterances or their synonyms, or mistranscriptions that are witnessed by the system. Of course, other ways of extension can also be used, while maintaining the spirit of the present principles.

The probability of action determination device290makes a determination, given a combination of words and gesture, the respective probabilities or likelihoods that particular system actions have been requested.

The multiple action arbiter295arbitrates between the multiple system actions when two or more are deemed to be applicable at a given time for a given session with a user. The arbiter can then pick one from among several of the multiple actions and, in an embodiment, can pick two or more, but less than all or even up to all, of the actions as they are deemed applicable. The determination of applicability can be based on the feedback and/or the confidence indications and/or the error indications.

In the embodiment shown inFIG. 2, the elements thereof are interconnected by a bus201. However, in other embodiments, other types of connections can also be used. Moreover, in an embodiment, at least one of the elements of system200is processor-based. Further, while the feedback generator250and confidence indication generator260are shown as separate elements, in other embodiments, these elements can be combined as one element. The converse is also applicable, where an element included in another element inFIG. 2can be implemented as a separate element in another embodiment. These and other variations of the elements of system200are readily determined by one of ordinary skill in the art, given the teachings of the present principles provided herein, while maintaining the spirit of the present principles.

FIGS. 3-4show a method300for learning intended user actions utilizing speech and gesture recognition, in accordance with an embodiment of the present principles.

At step305, choreograph a multimodal task to be supported.

At step310, specify (A) seed commands including the following two pairs: {action word, noun/pronoun for source} and {action word, noun, pronoun for target}, and (B) whether the source and target require pointing for disambiguation. Typically if a pronoun is used pointing is required for disambiguation of that pronoun.

At step315, add seed commands to a statistical knowledge repository283as valid source pairs and valid source individuals. The statistical knowledge repository283will keep track of words and word pairs used in conjunction with recognized gestures, and how often each of these lead to accepted system actions versus system actions which are then rejected via an undo request.

At step320, recognize, from the beginning of an utterance by a user, the anticipated gestural action (typically pointing) and at least one of the {action word, noun/pronoun for source}. This step is the selection phase of the command. A prototypical example is a user utterance of the form “Take this” while the user points to whatever “this” refers to. In this case, the action word is “Take”, the pronoun for the source is “this” and the anticipated gestural action is pointing.

At step325, generate an assumption that the user intends to select a given object (that is, issue a selection command), and provide an indication of the target of the selection (e.g., by highlighting the given object on a screen).

At step330, determine whether or not the user intends to proceed with the selection command. If so, then the method proceeds to step335. Otherwise, for example, if the user asks to undo the command, the method proceeds to step332to record the negative example in the statistical knowledge repository283, and returns to step320.

At step335, register, in the statistical knowledge repository383, the fact that the pair is valid and that the individual action word and noun or pronoun are valid in conjunction with the given gesture.

At step340, capture, from the user, a remainder of the utterance with a recognized gestural action (typically pointing) and at least one of the {action word, noun/pronoun for target}.

At step345, generate an assumption that the user intends for the prescribed action to be implemented and implement the prescribed action.

At step350, determine whether or not the user is continuing with the interaction (e.g., making further utterances, gesturing, interacting with others) or intends that the prescribed action be undone. If the user is continuing with the interaction, the method proceeds to step355. Otherwise, if the user intends that the prescribed action be undone the method continues to step352wherein the statistical knowledge repository registers the fact that the pair is not valid and that the individual action word and noun or pronoun are not valid in conjunction with the given gesture, and then the method returns to step340.

At step355, record a positive instance of {action word, noun/pronoun for target} and positive instances of individuals {action word}, {noun/pronoun for target} in the statistical knowledge repository383.

Our solution learns the different ways in which the desire for a given action, or system response, can be expressed, starting with one or more “seed expressions” for this action or system response, building up an ever-expanding lexicon through both positive and negative examples. In an embodiment, we exploit the following: the utterance behind any supported gestural action necessarily has two parts. For example, the expression “take this and put it there” has the two parts (1) “take this” and (2) “[and] put it there”, where one first points to whatever “this” is, and then points to whatever “that” is. At each juncture, the system gives feedback that it has understood what the user intends. Thus, if the “this” in the clause “take this” is an image on one screen, the moment “take this” is uttered with an accompanying gesture, the system highlights “this” to indicate its understanding of this and also that the user wishes to select “this” and do something with it. Following the expression “take this”, when the user points in the direction of “there” a mouse-like cursor appears on the surface to which the user is pointing to give feedback. Only when the user is sure the system understands where the user is pointing is the user expected to lock in on the expression “and move it there”, at which point the image originally in the “this” location is moved to the “there” location. In each segment of the utterance, the system looks for the user to employ an action word (verb) that the system recognizes and either a pronoun or noun that the system recognizes. If only one of the two (action word, or noun/pronoun) is present, then the system will still execute the action unless the system has accumulated evidence to the contrary, i.e., that the single action word or single noun does not generally indicate an intent to select the thing being pointed at. The user is always presented with an undo option, by saying a phrase like “no” or “no, I didn't mean that” or any word or words that convey the same concept of wanting to undo something. If the user is satisfied with the move, then the system learns a positive instance of the given new action word or noun/pronoun. If the user is unsatisfied, then a negative instance is credited to the given new action word or noun/pronoun. If a noun/pronoun or action word is successful a majority of the time, it is assumed to be valid with some confidence and error bars. The greater the error bars, the less the system will trust its conclusion, and especially in the case where an action word, noun/pronoun pair is deemed to only marginally be negatively indicative of an intent to select something. In an embodiment, addition probing will be done in an effort to narrow the error bars (in an exploration versus exploitation fashion).

In an embodiment, there can be probabilistic extensions to the present principles in the case that an implementation (e.g., system, method, and so forth) of the present principles is actively listening for multiple actions. For example, the system may be listening for the possibility that the user wants to move something to a new screen or alternatively add something to a shopping basket, or to a table of some sort for further analysis. In an embodiment, different and multiple listeners can be enabled at any given time as applicable or intended. The listener behind each action will have its own probability of acting, with each trading off exploration versus exploitation in bandit-like fashion. For example, the listener listening for the possibility that the user wants to move a selected item to a secondary screen may have X % confidence that the last utterance and accompanying gesture was indicative of this action, while the listener listening for something to be added to the shopping cart may have Y % confidence that the last utterance was indicative of this action, and the listener listening for something to be added to a given table may have Z % confidence. Each listener learns something by being wrong, with the least used listeners generally standing to gain the most. Thus, with some probability, each listener will opt to act, even when their assessment of their probability of being correct in their action is less than 50%. If several listeners decide to act there will be an arbitration. Each submits a confidence in its action and the one with highest confidence will be given the chance to act (assuming the actions are mutually exclusive, e.g. a move request and a delete request). In an embodiment, multiple and different listeners can be implemented by different and multiple threads, processes and/or devices being executed or being used concurrently in order to recognize and process the multiple actions.

In addition to seeding the system with sample utterances, the system may use various means of extending the sample utterances prior to or in parallel with training.

Consider the utterance “Select this and move it there”. The system breaks this utterance into two pieces: (i) “Select this,” and (ii) “move it there,” the former being what we call the selection directive, and the latter the move directive. The in-between word “and” is ignored. In an embodiment, up to two filler words of this sort are allowed. Each of these directives is deemed to be completed when the user is pointing and either a deictic is heard or a verb is heard (but no deictic) and a pause in speech occurs of at least 0.25 seconds. To estimate the probability, paccept, that the user actually means to select something, under the assumption that the system believes the user to be pointing at something that is selectable, in a sample embodiment the system uses the following formula:

paccept=pv+pp+v,p2⁢pv,p2+v,p2(1)
where: pv=probability of acceptance of the utterance given that the system has seen the verb; pp=probability of acceptance of the utterance given that the system has seen the pronoun; pv,p=probability of acceptance of the utterance given that the system has seen both the verb and the pronoun together; and |v,p|=number of times the system has seen both the verb and the pronoun together.

For the selection directive in the utterance, “Select this and move it there”, Equation (1) becomes:

Equation (2) is also used to estimate the probability of acceptance for the move directive, but under the assumption that there is a currently selected object and the user is pointing at a viable target. The probability of acceptance for the move directive of the utterance “Select this and move it there” has a slight subtlety but becomes:

In the case of this directive, there are two deictic terms, but the system stops processing as soon as the first of the deictic terms are transcribed. If the word “it” were mistranscribed to a non-deictic, the term “there” would be used instead.

Equation (1) is a weighted average of the three probabilities, pv, ppand pv,pwhere the weighting heavily favors pv,ponce sufficiently many instances of the associated {verb, pronoun} pair have been seen. If the system is wrong and the item is mistakenly selected, the user can do one of several things to tell the system that its reaction was incorrect. The user can say, “no, not that,” or in fact any short utterance containing either the word “no” or the word “not.” In this case the selected item is deselected. Secondly, the user can select something else. Finally, the user can do nothing with the selection and in 30 seconds the selection indicator will disappear.

In any of these cases of rejection, the associated probabilities of pv, ppand pv,pgo down, while the value of |v,p| is incremented by one. Non-rejection is assumed to be acceptance, in which case pv, ppand pv,pall go up, and, just like in the case of rejection, the value of |v,p| is incremented by one. The values pv, ppand pv,pare actually maintained as pairs of integers. For example, pvis maintained as a running fraction of the number of times a selection directive containing the given verb has been accepted (i.e., not rejected), over the total number of times a selection directive containing the given verb has been selected (i.e., either accepted or rejected).

In this embodiment the system only learns from false and true positives. The user may get irritated in the case of a false negative, in other words, the case that a user intended to select something but the system did not pick up on this fact, but in the present embodiment the system is not instrumented to pick up on such user frustration. Since the system cannot learn anything from false negatives, if paccept>=0.5 the system always opts to select. On the other hand, in an embodiment, if paccept<=0.5, the system adopts a modest bandit strategy, and draws a random number 0<q<1 and if

where lg( ) is the base-2 logarithm function, then the system will opt to select, thereby trading off a certain amount of current reward for learning and, hence, expected future dividends. As more instances of the particular {verb, pronoun} pair accumulate, and hence |v,p| goes up, the less eager the system is to explore.

Undoing the move directive is handled similarly. Note, however, that it is quite frequently the case that the move directive does not contain a deictic, for example, “Move this there,” “Move this over there,” “put this there,” and many other such examples. For this purpose the system uses a special pvø; that gives the probability of acceptance given that no verb was heard, pvø,p, giving the probability of acceptance of the utterance given the specified pronoun with no verb, and the analogous formula:

Although the case where only a deictic is uttered is more common, there are also cases where only a verb is uttered and also where only one of these parts of speech are uttered because of errors in the speech to text transcription. Thus, there are cases where one needs to use a term ppø, analogous to pvøgiven above.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

As shown inFIG. 5, computer system/server512in cloud computing node510is shown in the form of a general-purpose computing device. The components of computer system/server512may include, but are not limited to, one or more processors or processing units516, a system memory528, and a bus518that couples various system components including system memory528to processor516.

Computer system/server512typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server512, and it includes both volatile and non-volatile media, removable and non-removable media.

Program/utility540, having a set (at least one) of program modules542, may be stored in memory528by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules542generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

Computer system/server512may also communicate with one or more external devices514such as a keyboard, a pointing device, a display524, etc.; one or more devices that enable a user to interact with computer system/server512; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server512to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces522. Still yet, computer system/server512can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter520. As depicted, network adapter520communicates with the other components of computer system/server512via bus518. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server512. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

Referring now toFIG. 6, illustrative cloud computing environment650is depicted. As shown, cloud computing environment650comprises one or more cloud computing nodes610with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone654A, desktop computer654B, laptop computer654C, and/or automobile computer system654N may communicate. Nodes610may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment650to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices654A-N shown inFIG. 6are intended to be illustrative only and that computing nodes610and cloud computing environment650can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).