Modeling and projecting emotion and personality from a computer user interface6212502Abstract The invention is embodied in a computer user interface including an observer capable of observing user behavior, an agent capable of conveying emotion and personality by exhibiting corresponding behavior to a user, and a network linking user behavior observed by said observer and emotion and personality conveyed by said agent. The network can include an observing network facilitating inferencing user emotional and personality states from the behavior observed by the observer as well as an agent network facilitating inferencing of agent behavior from emotion and personality states to be conveyed by the agent. In addition, a policy module can dictate to the agent network desired emotion and personality states to be conveyed by the agent based upon user emotion and personality states inferred by the observing network. Typically, each network is a stochastic model. Each stochastic model is preferably a Bayesian network, so that the observing network is a first Bayesian network while the agent network is a second Bayesian network. Generally, the first and second Bayesian networks are similar copies of one another. Each of the two Bayesian networks include a first layer of multi-state nodes representing respective emotional and personality variables, and a second layer of multi-state nodes representing respective behavioral variables. Each one of the nodes includes probabilities linking each state in the one node with states of others of the nodes. More specifically, each one of the nodes in the first layer includes probabilities linking the states of the one first layer node to the states of nodes in the second layer. Similarly, each one of the nodes in the second layer include probabilities linking the states of the one second layer node to states of nodes in the first layer. Claims What is claimed is: Description BACKGROUND OF THE INVENTION
TABLE I
Concept Paraphrases
greeting hello greetings howdy
hi there hey
yes yes absolutely yeah
I guess so I think so for sure
suggest I suggest that you you should
perhaps you would like to let's
maybe you could
The preferred embodiment of the invention models the influence of emotion and personality on wording choice in two stages of a Bayesian network, as shown in FIG. 3. The first stage captures the relationship between personality and emotion and various classes of expressive style. The first stage 305, in the current implementation of the invention, consists of a first layer of nodes 310, 315, 320, 325 representing (respectively) the emotional/personality states of valence, arousal, dominance and friendliness and a second layer of nodes 330, 335, 340, 345 representing, respectively, the expression states of active, positive, terse, and strong. The expression nodes 330-345 are successors of the emotion and personality nodes 310-325, and capture the probability that the individual would express themselves in an active, positive, strong, and/or terse manner given emotional/personality states. Each of these nodes are binary valued, true or false. Thus, the first stage 305 captures the degree to which an individual with a given personality and in a particular emotional state will tend to communicate in a particular style. (A current implementation includes another expressive style, "formal", which is treated in a manner parallel to "terse" or "active".) A second stage 350 captures the degree that each paraphrase actually is active, positive, terse, etc. The second stage 350, however, says nothing about the individual, but rather reflects a general cultural interpretation of each paraphrase, that is the degree to which that phrase will be interpreted as active, positive, terse, and so on by a speaker of American English. A node such as "Concept Active" is also binary valued, and is true if the paraphrase would be interpreted as "Active" and false otherwise. The second stage 350 consists of interpretation nodes 352, 354, 356, 358 representing, respectively, the probability that a particular concept or paraphrase from a concept node 360 would be interpreted as having an active, positive, strong and terse expressive interpretation. Finally there is a match layer 370 consisting of a set of nodes 372, 374, 376, 378 for the expression attributes of active, positive, strong and terse, respectively. The nodes 372-378 represent whether a particular expressive interpretation of the paraphrase matches the intended expressive style by the individual for each component. The node 372, "Match Active" has value true if and only if the values of the "Concept Active" node 352 and the "Active Expression" node 330 are the same. The "Match" node 380 at the bottom (output) of the network is simply a Boolean conjunction that has value "true" when all its parents (the match nodes for each component of expressive style) are true. Thus, the Bayesian belief network fragment of FIG. 3 indicates (1) the relationship of emotion and personality on expressive style, (2) the probability that a modeled concept will be interpreted as a particular style, and (3) whether the interpretation matches the intent for each component and whether they match on all components. In carrying out the present invention for a particular application (such as troubleshooting), one must generate a network fragment of the type illustrated in FIG. 3 for each possible conceptual command or element in the vocabulary of the application. These fragments are merged into a global Bayesian network capturing the dependencies between the emotional state, personality, natural language, and other behavioral components of the model. A portion of such a Bayesian network, i.e., one consisting of merged fragments, is shown in FIG. 4. The various fragments differ only in the assessment of the paraphrase scorings, that is the probability that each paraphrase will be interpreted as active, strong, etc. There are five assessments needed for each alternative paraphrase for a concept (the ones mentioned earlier, plus a formality assessment). Note that the size of the belief network representation grows linearly in the number of paraphrases (the number of concepts modeled times the number of paraphrases per concept). Thus, referring to FIG. 4, the expressive style layer 305 is as described with reference to FIG. 3 and is connected in common to the merged fragments. One of the fragments consists of the concept input node 360' for "no" corresponding to the concept input node 360 of FIG. 3, a "no" interpretation layer 350' corresponding to the interpretation layer 350 of FIG. 3, a "no" match layer 370' corresponding to the match layer 370 of FIG. 3 and a "no" output match node 380' corresponding to the match output node 380 of FIG. 3. Another one of the fragments consists of the concept input node 360" for "yes" corresponding to the concept input node 360 of FIG. 3, a "yes" interpretation layer 350" corresponding to the interpretation layer 350 of FIG. 3, a "yes" match layer 370" corresponding to the match layer 370 of FIG. 3 and a "yes" output match node 380" corresponding to the match output node 380 of FIG. 3. One could have each of the expressive style nodes pointing directly into the concept node, thus creating, for each concept, a multi-state node with five parents. The assessment burden in this structure would be substantial, and a causal independence assumption such as noisy-or is not appropriate. The preferred embodiment of the present invention reduces this assessment burden, and also allows modular addition of new "expressive style" nodes. If one adds a new expressive style node to the network (such as, for example, "cynical" ), then the only additional assessments needed are the "cynical" interpretation nodes of each concept paraphrase. Two examples of assessments of this type are shown in FIGS. 5A and 5B respectively. In FIG. 5A, various candidate greetings in the column labelled "greet" (e.g., "hello", "hi there", "howdy") are assessed for their terseness and assigned an individual probability (under the columns "true" and "false" for being perceived as being terse, corresponding to the operation of the "terse" interpretation node 358 of FIG. 3. In FIG. 5B, various candidate greetings are assessed for their activeness and assigned an individual probability for being perceived as being active, corresponding to the operation of the "active" interpretation node 352 of FIG. 3. In addition to reducing the assessment burden, such features of the Bayesian network structure of the invention make it easy to extend the model for new concepts and dimensions of expressive style. Inference: As discussed above, the merged Bayesian network model relates emotional state and personality to wording choice, speech characteristics, input style characteristics, and body language/movements. Most of these observable expressions are modeled as being directly caused by the components of emotion and personality. For choice of paraphrase we make an additional assumption in using the Bayes net structure described above: the individual being modeled choose wording so as to match the intended interpretation with their current desired expressive style. Thus we are imputing some choice behavior to the individual. This behavior is incorporated into inference by setting the "match" nodes to true before updating probabilities. Students of Bayesian inference will note that in the network in FIG. 4, observing "match" will serve to create the desired dependency between the components of emotion and personality and the choice of paraphrase. Under this interpretation, the model captures a decision model regarding word selection. The selection of a paraphrase is done such that it maximizes the probability of a match between intended expressive style and interpretation, given all previous observations regarding gesture, speech characteristics, and wording choice. We implement this approach in the network by setting each "Match" node to true. By setting the prior probability of the paraphrases in each concept node to a uniform distribution over the alternatives, application of a standard Bayesian network inference algorithm will generate a posterior distribution over word choices consistent with "match" being true. The paraphrase that has the maximum posterior probability is the one that maximizes the probability of "match" being true. We discuss the use of this technique more fully in the next section where we describe using this model for diagnosis (to determine what mood the user is in) as well as for generating behavior (to determine what should the agent say if he is in a good mood). Reasoning Architecture: In providing a user interface of the invention having emotion and personality, it is preferable to maintain two copies of the emotion/personality Bayesian network model of FIG. 2. One copy is used to diagnose the user, the other to generate behavior for the "agent" which is projected back to the user as a voice or as a voice and image, for example. Such an architecture is illustrated in FIG. 6. Referring to FIG. 6, the Bayesian user network model 610 (a copy of the network of FIG. 2) receives inputs from the user interface representing observations of the user's behavior (loud and angry voice tones, or a calm and quiet voice, for example).The model 610 uses such observations to infer the emotional state and personality of the user and sends this information to a response policy module 620 (hereinafter referred to as a "policy module"). The policy module 620 governs the agent behavior or response exhibited to the user (by the computer's user interface) in accordance with, among other things, the user's emotional state and personality. The policy module 620 may be an independent entity but, more likely, is embedded in an application 630 run by the computer. The policy module 620 governs a Bayesian agent network model 640 (another copy of the network of FIG. 2) and informs network 640 what emotional and personality state is to be projected to the user by the agent. The Bayesian agent model 640 responds to this input (defining the emotional and personality state to be projected) by inferring an appropriate behavior for the agent to perform. The observation of the user's behavior is accomplished through a observation interface 650 which can include a microphone and a speech synthesizer, for example. Furthermore, the observation interface 650 may monitor the user's inputs to the application from the user's keyboard and mouse, for example. The agent behavior interface 660 may be implemented by speech made by the computer to the user, as one example. Operation: The architecture of FIG. 6 is operated in accordance with the following steps: (1) Observe. This step refers to recognizing an utterance as one of the possible paraphrases for a concept. At a given point in the dialogue, for example after asking a yes/no question, the speech recognition engine is listening for all possible paraphrases for the speech concepts "yes" and "no". When one is recognized, the corresponding node in the user Bayesian network is set to the appropriate value. (2) Update. In this step, the invention employs a standard probabilistic inference algorithm to update probabilities of personality and emotional state in the Bayesian user model 610 given the observations. All concept "match" nodes are set to true. (3) Agent Response. The linkage between the user and agent network models 610, 640 is embedded in the policy module 620. The policy module 620 is the mapping from the updated probabilities of the emotional states and personality of the user (furnished by the Bayesian user model 610) to the desired emotional state and personality of the agent. The policy module 620 can be designed to develop an empathetic agent, whose mood and personality matches that of the user, or a contrary agent, whose emotions and personality tend to be the exact opposite of the user, as two possible examples. Research has indicated that users prefer a computerized agent to have a personality makeup similar to their own, so by default the present implementation of the invention employs a policy module corresponding to an agent having an empathetic response policy. The design of the policy module 620 is up to the system designer, the present invention being compatible with any appropriate policy module. (4) Propagate. The Bayesian agent network model 640 facilitates probabilistic inference to generate probability distributions over various parameters of behavior including paraphrases, animations, speech characteristics, and so on, consistent with the emotional state and personality set by the policy module 620. Here, again, the "Match" node is set to value true. (5) Generate Behavior. At a given stage of the dialogue, the task model may dictate that the agent express a particular concept, for example "greet" or "regret". The behavior module then consults the Bayesian agent network model 640 for the current distribution over the possible paraphrases for expressing that concept. Preferably, the paraphrase with the maximum probability from that distribution is selected for agent to speak. This string is then passed to the text-to speech engine in the user interface to generate the audible output. For application of the model in computer systems, the current embodiment applies to a command and control scenario where there are relatively few utterances and responses that the agent needs to recognize and respond to. The invention may also be applied to the more general case of dictation-style speech recognition with more complete language grammars. Bayesian User Model: FIG. 7 illustrates the Bayesian user model 610 which discerns the user's mood (emotional state and personality) from the observed user behavior. The Bayesian network of FIG. 7 corresponds to the Bayesian network of FIG. 2, but includes a more robust set of features. The two emotion variables are represented by the an node 702 and a valence node 704. The two personality variables are represented by a dominance node 706 and a friendliness node 708. The states of the four nodes 702-708 are the inferred emotional/personality state of the user. They receive inputs from nodes representing different modes of observed behavior, as follows. The speech of the user can be analyzed in a conventional speech engine to deduce the user's speech volume, speech speed (word rate), pitch and the response speed or promptness, represented by nodes 710, 712, 714, 716, respectively. The nodes 710, 712, 714, 716 receive their inputs from a user interface 718 which may include a speech engine (not shown) which can determine the states of the corresponding variables (i.e., speech speed, volume, speed, pitch and response speed). The four emotion/personality nodes 702-708 also receive inputs from five word attribute nodes including an active word node 720, a positive word node 722, and a strong word node 724 and a terse word node 726. In addition, a third personality node may be defined, namely a social class node 730 which receives its input from another word attribute node, namely a formal word node 728. The five word attribute nodes receive their inputs from a language submodel 732, which corresponds to the interpretation nodes 352-358 of the interpretation layer 350 of the network of FIG. 3. The language submodel 732 assigns to each word or phrase uttered by the user a five-attribute score representing the states of the five word attribute nodes 720-728. If the user interface 718 has a camera and has the ability to view the user and discern the user's facial expressions, then a facial expression node 734 and a facial expression strength node 736 may be included which receive their inputs from the user interface 718 and provide outputs to appropriate ones of the emotion/personality nodes 702-708, as illustrated in FIG. 7. Alternatively, the expression and expression strength nodes 734 and 736 may take their inputs from the speech engine sensing voice intonations or expressions, and as a second alternative they may take their inputs from both observed facial expression as well as observed voice expression. Likewise, if the user's posture and gestures may be observed by the interface 718, then posture and gesture nodes 740, 742 may be included as well, receiving their inputs from the interface 718 and providing outputs to appropriate ones of the emotion/personality nodes 702-708 as shown in FIG. 7. States of the Emotion/Personality Variables: The nodes of the Bayesian network of FIG. 7 correspond to variables whose possible states are selected to represent real transitions in personality, emotion and behavior. Of course, the number of variables and the number of states of each variable could be such large numbers as to render the model assessment task impractical or impossible. Therefore, the variables have been limited in the present embodiment to a relatively small number while their number of states have been limited as well. For example, some variables could be continuous, in which case they have been "quantized" to a limited number of discrete states. The selection of variables and their states has followed no mathematical derivation but is based rather upon a heuristic approach using known principles of psychology discussed previously herein. FIGS. 8A-8D tabulate the states of the four emotion/personality nodes 702-708. The rationale for this structure is well known. One can construct an "emotion" 2-dimensional space defined by the two emotion 3-state variables of valence and arousal as illustrated in FIG. 9, in which the emotional state is determined by the states of the two emotion variables of arousal and valence. Thus, for example, a joyful emotional state occurs when arousal reaches an excited state and valence reaches a positive state, while a sad emotional state occurs when arousal falls to a passive state and valence falls to a negative state. Likewise, one can construct a "personality" 2-dimensional space defined by the two personality 3-state variables of dominance and friendliness as illustrated in FIG. 10. Thus, for example, a gregarious personality state is characterized by a high or "dominant" state of the dominance variable and a high or "friendly" state of friendliness, while an aloof personality state is characterized by a low or "submissive" state of the dominance variable and a low or "unfriendly" state of the friendliness variable. States of the Expression Variables: FIGS. 11A and 11B tabulate the states of the expression node 734 and the expression strength node 736. The expression node 734 has six states in FIG. 11A, namely happy, surprise, fear, anger, sad and disgust while the expression strength node 736 has been quantized to three states in FIG. 11B, namely high, medium and low. FIG. 12 tabulates the probabilities stored in the expression node 734 in accordance with a current implementation of the invention. In this implementation, the expression node is linked to only three of the four emotion/personality nodes 702-708, namely the valence, arousal and friendliness nodes 702, 704, 706. FIG. 12 reflects the 27 possible combinations of the states of the three 3-state nodes 702, 704, 706, in that the three states of the arousal node 704 are set forth for each state of the valence node 702 while the three states of the friendliness node 706 are set forth for each of the nine combinations of the valence and arousal node states. For each of the 27 state combinations thus tabulated, a probability is assigned for each one of the five states of the expression node 734, namely happy, surprise, fear, anger, sad and disgust, 734a-734f respectively in FIG. 12. Thus, FIG. 12 tabulates 5(27)=135 individual probabilities. The probability values illustrated in FIG. 12 were assigned heuristically, and a skilled worker could redefine these values in accordance with another heuristic evaluation, and the invention may be carried out with any appropriate array of probability values for the 135 possibilities illustrated in FIG. 12. While FIG. 12 illustrates one example of how to construct the expression node 734 in the Bayesian network of FIG. 4, it also illustrates a methodology for constructing all of the nodes of FIG. 8: A probability is heuristically assigned by the skilled worker to each possible state of a node for each possible permutation of the states of its antecedent nodes, based upon a qualitative knowledge of psychology. The probabilities thus assigned form an array of probability values which is stored in the corresponding node in the Bayesian network of FIG. 4. In addition, if data is available, these conditional probabilities can be "learned" or estimated from observational data. States of the Word Interpretation Variables: The five variables characterizing word interpretation, represented by the active, positive, strong, terse and formal word nodes 720, 722, 724, 726 and 728, respectively could each have any number of states convenient to the skilled worker. In the present implementation, the assessment burden is greatly reduced by constructing these nodes to have only two states, namely true and false. These binary states of the five word interpretation variables 720-728 are tabulated in FIGS. 13A-13D, respectively. The skilled worker assigns a probability to each state of the five word interpretation nodes 720-728 for each combination of states of the emotion/personality nodes 702-708, 728. These assignments are independent of the chosen words or phrases, and simply refer to the type of phrases or words most likely employed to express a particular emotion/personality state. The resulting probabilities are stored in appropriate ones of the five word interpretation nodes 720-728. The word interpretation nodes 720-728 interact between the five emotion/personality nodes 702-708,730 and the language submodel 732. Language Submodel: For each word or phrase to be interpreted by the Bayesian network of FIG. 3, a probability assessment is made by the skilled worker whether or not that word or phrase is active, another decision is made whether that same word or phrase is positive, and so forth, a probability is assigned for each of the five word interpretation attributes of active, positive, strong, terse and formal. As previously discussed in this specification, it is preferable that these decisions are made by the skilled worker in light of common language usage, independently of any personality type or emotional state of the user. The resulting assessments are stored as numerical information in the language submodel 732, as shown in FIG. 5A and 5B. FIG. 14 illustrates one possible implementation of the language submodel 732. The language submodel 732 includes a candidate phrase look-up table 810 which can receive a concept to be expressed (such as "greeting") and look up the various alterative expressions of that concept (such as those listed in FIGS. 5A and 5B). A 5-attribute score look-up table 820 receives each candidate phrase from the candidate phrase look-up table 810 and finds, for each candidate phrase, a score (e.g., a binary score such as 1 or 0) for each of the five word interpretation attributes of active, positive, strong, terse and formal. The result is output by the 5-attribute score look-up table 820 as a 5-score data structure 830. A match or similarity comparison module 840 compares each entry in the 5-score data structure with the state of the corresponding one of the five word interpretation nodes 720-728. In a simple embodiment, the match module 840 scores a "1" for each of the five entries in the data structure 830 having the same binary value as the state of the corresponding one of the word interpretation node 720-728. A candidate probability module 850 sums the scores for each candidate phrase and stores the result as a probability in a memory 860. After all the candidate phrases have been thus evaluated, a pick winner module 870 reviews all of the probabilities stored in the memory 860 and picks the candidate phrase from the look-up table 810 having the "best" probability in the memory 860. In the simplest implementation, the "best" probability is the highest probability (i.e., the highest sum of scores). Other implementations may employ a different rule to define the "best" score. FIG. 15 illustrates one example of how the score-sums or probabilities stored in the memory 860 may be distributed over the possible candidate phrases for a given concept for a given state of the five word interpretation nodes 720-728. The number of candidate phrases is, theoretically, unlimited, while the probability or score sum stored in the memory 860 for each candidate ranges from 0 to 5 in the binary implementation. States of the Speech and Gesture Variables: The speech nodes, including speech speed, speech pitch and speech volume have the states illustrated in FIGS. 16. The response speed node, which characterizes how fast or quick a response is given, is reflected by the response speed node whose states are tabulated in FIG. 16D. The posture node has five states tabulated in FIG. 16E while the gesture node has four states tabulated in FIG. 16F. The social class node (which is linked to the word formality node) has three states tabulated in FIG. 16G. The Bayesian Agent Network Model: FIG. 17 illustrates the Bayesian Agent Network Model 640 of FIG. 6. Preferably, the internal structure of the model 640 is generally the same as the user model 610 of FIG. 7. The main difference is that the model is operated in the opposite direction. Specifically, the emotion/personality nodes 702-708, 730 have their states dictated by the policy module 620 and they dictate, in turn, the states of the other nodes controlling behavior. Thus, the emotion/personality nodes 702-708, 730 receive inputs from the policy module 620 and have outputs connected to the inputs of the other nodes in the network. For example, the speech interpretation nodes 720-728 have their inputs connected to the emotion/personality nodes and their outputs connected to the language submodel 732. The language submodel 732, as well as the remaining nodes in the network of FIG. 17 have their outputs connected to the agent behavior interface 660. The behavior interface 660 governs, for example, the audible output of the user interface of the computer. Working Example of a Bayesian Network in the Invention: The two Bayesian networks of FIGS. 7 and 17 are used to perform inferencing in opposite directions, as mentioned above. Specifically, the Bayesian network of FIG. 7 is employed to perform inferencing from the behavioral nodes toward the emotion/personality nodes so as to infer an emotional/personality state from observed behavior, while the Bayesian network of FIG. 17 is employed to perform inferencing from the emotion/personality nodes toward the behavioral nodes so as to infer a behavioral state from a dictated emotional/personality state. However, the structure of the two networks can be generally the same. The network of FIG. 7 is updated to reflect the changing moods of the human user, while the network of FIG. 17 is updated to reflect the immediate goals of the policy module. Therefore, the states of the various nodes in the two networks of FIGS. 7 and 17 will differ necessarily as operation proceeds, but the internal structure, including the probabilities stored in each node, are preferably the same in both networks (i.e., the network of FIG. 7 and the network of FIG. 17). A working example of such a structure is now set forth. It is understood that the skilled worker may improve and modify greatly this structure in carrying out the present invention. The exemplary structure is defined below in tabular form, with the states of the variables being listed in the order in which their probabilities are listed for each node for each configuration of states of the parent nodes. States of the Nodes node Valence "Negative", "Neutral", "Positive" node Arousal "Passive", "Neutral", "Excited" node Dominant "Submissive", "Neutral", "Dominant" node Friendly "Unfriendly", "Neutral", "Friendly" node Expression "Happy", "Surprised", "Fear", "Anger", "Sad", "Disgust" node ExpressionStrength "High", "Medium", node Positive "yes", "no" category: "wordstyle"; node Active "yes", "no" category: "wordstyle"; node Strong "yes" "no" category: "wordstyle"; node Terse "yes", "no" category: "wordstyle"; node Formal "yes", "no" category: "wordstyle"; node SpeechVolume "% Vol=2000%", "% Vol=30000%", "% Vol=3500%" node SpeechSpeed "% spd=125%", "% spd=150%", "% spd=175%" node SpeechPitch "Normal", "Raised" node SocialClass "High Society", "White Collar", "Blue Collar" node ResponseSpeed "Fast", "Normal", "Slow" node posture "Neutral", "Slouch", "Stiff", "Impatient", "Restless" node Gesture "Relax", "Wave", "Point", "Beat", "Fold", "HandsBehind", "Shrug" node PersonalityAssed name: "PersonalityConfidence"; "Low", "Medium", "High" Structure of the Nodes probability(Expression .vertline. Valence, Arousal, Friendly) (0, 0, 0): 0.0178117, 0.0226973, 0.254758, 0.362545, 0.223613, 0.118575; (0, 0, 1): 0.0178117, 0.0226973, 0.329262, 0.293919, 0.255598, 0.0807123; (0, 0, 2): 0.0178117, 0.0226973, 0.360941, 0.232316, 0.317659, 0.0485749; (0, 1, 0): 0.0178117, 0.0361324, 0.22743, 0.422316, 0.1465 9, 0.14972; (0, 1, 1): 0.0178117, 0.0361324, 0.237812, 0.435827, 0.175674, 0.0967429; (0, 1, 2): 0.0178117, 0.0361324, 0.285903, 0.382239, 0.201476, 0.0764376; (0, 2, 0): 0.0201018, 0.0836133, 0.145369, 0.443461, 0.108804, 0.198651; (0, 2, 1): 0.0201018, 0.0836133, 0.184224, 0.492544, 0.116057, 0.10346; (0, 2, 2): 0.0201018, 0.0836133, 0.215369, 0.454529, 0.168118, 0.0582692; (1, 0, 0): 0.11799, 0.212612, 0.149246, 0.149246, 0.22166, 0.149246; (1, 0, 1): 0.360484, 0.127903, 0.127903, 0.127903, 0.127903, 0.127903; (1, 0, 2): 0.679995, 0.0651819, 0.0637057, 0.0637057, 0.0637057, 0.0637057; (1, 1, 0): 0.0971815, 0.169679, 0.169679, 0.169679, 0.224102, 0.169679; (1, 1, 1): 0.259975, 0.134693, 0.153689, 0.160155, 0.161361, 0.130126; (1, 1, 2): 0.528219, 0.0858694, 0.09798, 0.102102, 0.102871, 0.0829581; (1, 2, 0): 0.0544722, 0.180074, 0.180074, 0.180074, 0.225232, 0.180074; (1, 2, 1): 0.165293, 0.166941, 0.166941, 0.166941, 0.166941, 0.166941; (1, 2, 2): 0.30743, 0.138514, 0.138514, 0.138514, 0.138514, 0.138514; (2, 0, 0): 0.149254, 0.179469, 0.154404, 0.154404, 0.208064, 0.154404; (2, 0, 1): 0.281912, 0.209476, 0.127153, 0.127153, 0.127153, 0.127153; (2, 0, 2): 0.494554, 0.16575, 0.0849239, 0.0849239, 0.0849239, 0.0849239; (2, 1, 0): 0.301301, 0.193078, 0.117842, 0.117842, 0.152094, 0.117842; (2, 1, 1): 0.473561, 0.147629, 0.0947026, 0.0947026, 0.0947026, 0.0947026; (2, 1, 2): 0.590349, 0.131858, 0.0694485, 0.0694485, 0.0694485, 0.0694485; (2, 2, 0): 0.380913, 0.179435, 0.102019, 0.102019, 0.133594, 0.102019; (2, 2, 1): 0.628553, 0.120285, 0.0627905, 0.0627905, 0.0627905, 0.0627905; (2, 2, 2): 0.841709, 0.0694659, 0.0222063, 0.0222063, 0.0222063, 0.0222063; probability(ExpressionStrength .vertline. Arousal, Dominant) (0, 0): 0.154784, 0.293715, 0.551501; (0, 1): 0.251501, 0.302341, 0.446158; (0, 2): 0.333333, 0.333333, 0.333333; (1, 0): 0.246463, 0.356845, 0.396692; (1, 1): 0.333333, 0.333333, 0.333333; (1, 2): 0.399288, 0.348753, 0.251959; (2, 0): 0.333333, 0.333333, 0.333333; (2, 1): 0.399899, 0.354669, 0.245432; (2, 2): 0.545089, 0.30257, 0.152341; probability(Positive .vertline. Valence, Friendly) (0, 0): 0.0212214, 0.978779; (0, 1): 0.101145, 0.898855; (0, 2): 0.298626, 0.701374; (1, 0): 0.0719847, 0.928015; (1, 1): 0.355496, 0.644504; (1, 2): 0.73855, 0.26145; (2, 0): 0.328855, 0.671145; (2, 1): 0.614504, 0.385496; (2, 2): 0.976183, 0.0238166; probability(Active .vertline. Arousal, Dominant) (0, 0): 0.124657, 0.875343; (0, 1): 0.23771, 0.76229; (0, 2): 0.429542, 0.570458; (1, 0): 0.254886, 0.745114; (1, 1): 0.506412, 0.493588; (1, 2): 0.820611, 0.179389; (2, 0): 0.604275, 0.395725; (2, 1): 0.75855, 0.24145; (2, 2): 0.90397, 0.0960303; probability(Strong .vertline. Arousal, Dominant) (0, 0): 0.184885, 0.815115; (0, 1): 0.300076, 0.699924; (0, 2): 0.779618, 0.220382; (1, 0): 0.40084, 0.59916; (1, 1): 0.519618, 0.480382; (1, 2): 0.869848, 0.130152; (2, 0): 0.582137, 0.417863; (2, 1): 0.726107, 0.273893; (2, 2): 0.918245, 0.0817554; probability(Terse .vertline. Dominant, Valence, Friendly) (0, 0, 0): 0.866108, 0.133892; (0, 0, 1): 0.721299, 0.278701; (0, 0, 2): 0.61794, 0.38206; (0, 1, 0): 0.733284, 0.266716; (0, 1, 1): 0.530917, 0.469083; (0, 1, 2): 0.325726, 0.674274; (0, 2, 0): 0.592367, 0.407633; (0, 2, 1): 0.413742, 0.586258; (0, 2, 2): 0.287863, 0.712137; (1, 0, 0): 0.799542, 0.200458; (1, 0, 1): 0.667023, 0.332977; (1, 0, 2): 0.531298, 0.468702; (1, 1, 0): 0.692443, 0.307557; (1, 1, 1): 0.450458, 0.549542; (1, 1, 2): 0.263359, 0.736641; (1, 2, 0): 0.321985, 0.678015; (1, 2, 1): 0.321985, 0.678015; (1, 2, 2): 0.333206, 0.666794; (2, 0, 0): 0.877252, 0.122748; (2, 0, 1): 0.877252, 0.122748; (2, 0, 2): 0.877252, 0.122748; (2, 1, 0): 0.680535, 0.319465; (2, 1, 1): 0.680535, 0.319465; (2, 1, 2): 0.680535, 0.319465; (2, 2, 0): 0.512138, 0.487862; (2, 2, 1): 0.512138, 0.487862; (2, 2, 2): 0.512138, 0.487862; probability(Formal .vertline. SocialClass) (0): 0.886489, 0.113511; (1): 0.465725, 0.534275; (2): 0.204504, 0.795496; probability(SpeechVolume .vertline. Arousal, Dominant:) (0, 0 ): 0.668601, 0.277685, 0.0537145; (0, 1): 0.546082, 0.400204, 0.0537145; (0, 2): 0.446998, 0.499288, 0.0537145; (1, 0): 0.0544786, 0.894708, 0.0508136; (1, 1): 0.0544786, 0.894708, 0.0508136; (1, 2): 0.0544786, 0.894708, 0.0508136; (2, 0): 0.0534097, 0.500204, 0.446386; (2, 1): 0.0534097, 0.396387, 0.550203; (2, 2): 0.0534097, 0.275319, 0.671272; probability(SpeechSpeed .vertline. Arousal, Dominant) (0, 0): 0.751654, 0.194784, 0.0535616; (0, 1): 0.665929, 0.280509, 0.0535616; (0, 2): 0.549822, 0.396616, 0.0535616; (1, 0): 0.207532, 0.724402, 0.0680658; (1, 1): 0.0540968, 0.87944, 0.0664627; (1, 2): 0.0476082, 0.740967, 0.211425; (2, 0): 0.0460052, 0.400738, 0.553257; (2, 1): 0.0460052, 0.285166, 0.668829; (2, 2): 0.0460052, 0.207761, 0.746234; probability(SpeechPitch .vertline. Arousal) (0): 0.798703, 0.201297; (1): 0.5, 0.5; (2): 0.328168, 0.671832; probability(SocialClass) 0.0960815, 0.806463, 0.0974553; probability(ResponseSpeed .vertline. Arousal, Dominant) (0, 0): 0.00989832, 0.0894406, 0.900661; (0, 1): 0.0129008, 0.389568, 0.597531; (0, 2): 0.0499746, 0.500891, 0.449134; (1, 0): 0.0319595, 0.640051, 0.327989; (1, 1): 0.0349365, 0.935776, 0.0292871; (1, 2): 0.333333, 0.640357, 0.0263098; (2, 0): 0.454555, 0.496539, 0.0489057; (2, 1): 0.601883, 0.392952, 0.00516483; (2, 2): 0.895853, 0.0915773, 0.0125696; probability(Posture .vertline. Valence, Dominant, Friendly) (0, 0, 0): 0.246991, 0.58823, 0.052014, 0.0796421, 0.0331224; (0, 0, 1): 0.255657, 0.608868, 0.0538389, 0.047352, 0.0342845; (0, 0, 2): 0.341415, 0.502941, 0.0444723, 0.039114, 0.0720581; (0, 1, 0): 0.212395, 0.327482, 0.140184, 0.140363, 0.179575; (0, 1, 1): 0.292453, 0.445655, 0.0798784, 0.0702542, 0.11176; (0, 1, 2): 0.348018, 0.442555, 0.0732893, 0.033598, 0.10254; (0, 2, 0): 0.227605, 0.25872, 0.283321, 0.199831, 0.0305227; (0, 2, 1): 0.250647, 0.284912, 0.252706, 0.178122, 0.0336127; (0, 2, 2): 0.2921, 0.332031, 0.21157, 0.125127, 0.0391716; (1, 0, 0): 0.731904, 0.123253, 0.0981535, 0.0326996, 0.0139894; (1, 0, 1): 0.767437, 0.129237, 0.0664754, 0.0221819, 0.0146685; (1, 0, 2): 0.795237, 0.133919, 0.0326589, 0.0229854, 0.0151999; (1, 1, 0): 0.788574, 0.0684533, 0.0905728, 0.0373275, 0.0150725; (1, 1, 1): 0.818703, 0.0710687, 0.0709161, 0.0236637, 0.0156484; (1, 1, 2): 0.840363, 0.0729489, 0.0463362, 0.0242897, 0.0160624; (1, 2, 0): 0.625162, 0.081973, 0.148075, 0.0968897, 0.0479007; (1, 2, 1): 0.668007, 0.0875911, 0.129177, 0.0640408, 0.0511836; (1, 2, 2): 0.704837, 0.0924202, 0.0987455, 0.0499922, 0.0540055; (2, 0, 0): 0.196144, 0.295268, 0.201351, 0.116612, 0.190625; (2, 0, 1): 0.217307, 0.327126, 0.197928, 0.0875395, 0.170099; (2, 0, 2): 0.244668, 0.368315, 0.172976, 0.0733075, 0.140734; (2, 1, 0): 0.203586, 0.119234, 0.329657, 0.110359, 0.237164; (2, 1, 1): 0.230501, 0.11371, 0.294418, 0.0928544, 0.268517; (2, 1, 2): 0.223655, 0.168063, 0.285673, 0.0620676, 0.260541; (2, 2, 0): 0.168096, 0.0867761, 0.364067, 0.185242, 0.195819; (2, 2, 1): 0.193637, 0.0999605, 0.341657, 0.139173, 0.225573; (2, 2, 2): 0.208272, 0.107516, 0.31986, 0.121729, 0.242622; probability(Gesture .vertline. Valence, Friendly) (0, 0): 0.224919, 0.0214209, 0.214209, 0.0321313, 0.214209, 0.23563, 0.0574816; (0, 1): 0.189346, 0.0256489, 0.150178, 0.101735, 0.251902, 0.255579, 0.0256106; (0, 2): 0.882629, 0.0163356, 0.0213402, 0.0213402, 0.0165993, 0.017252, 0.0245041; (1, 0): 0.189346, 0.0256489, 0.150178, 0.101735, 0.251902, 0.255579, 0.0256106; (1, 1): 0.882629, 0.0163356, 0.0213402, 0.0213402, 0.0165993, 0.017252, 0.0245041; (1, 2): 0.384136, 0.0725131, 0.071723, 0.238388, 0.0830207, 0.102319, 0.0478998; (2, 0): 0.882629, 0.0163356, 0.0213402,.0.0213402, 0165993, 0.017252, 0.0245041; (2, 1): 0.384136, 0.0725131, 0.071723, 0.238388, 0.0830207, 0.102319, 0.0478998; (2, 2): 0.0816913, 0.17196, 0.0540413, 0.334838, 0.0548546, 0.0635674, 0.239047; ) Other Embodiments of the Invention: While the invention has been described with reference to a preferred embodiment in which the model networks are Bayesian networks, the invention may be carried out in a different manner using models other than Bayesian networks, such as Markov chains. In fact, the invention is not confined to any particular model type by may be carried out using any suitable stochastic model. While the invention has been described in detail with reference to preferred embodiments, it is understood that variations and modifications thereof may be made without departing from the true spirit and scope of the invention.
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