Patent Publication Number: US-11645473-B2

Title: End-of-turn detection in spoken dialogues

Description:
BACKGROUND 
     The subject disclosure relates to machine learning, and more specifically, to speech recognition and processing. 
     SUMMARY 
     The following presents a summary to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements, or delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, systems, computer-implemented methods, and/or computer program products that facilitate predicting a future source of dialog. 
     According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory, the computer executable components comprising a speech receiving component that can receive a spoken dialogue from a first entity. The computer executable components can further comprise a speech processing component that can employ a network that can concurrently process a transition type and a dialogue act of the spoken dialogue to predict a source of a subsequent spoken dialogue. 
     According to another embodiment, a computer-implemented method can comprise receiving, by a system operatively coupled to a processor, a spoken dialogue from a first entity. The computer-implemented method can further comprise predicting, by the system, a source of a subsequent spoken dialogue by employing, by the system, a network that concurrently processes a transition type and a dialogue act. 
     According to another embodiment, a computer program product that can facilitate predicting a source of a subsequent spoken dialogue is provided. The computer program product can comprise a computer readable storage medium having program instructions embodied therewith, the program instructions can be executable by a processor to cause the processor to receive, by the processor, a spoken dialogue from a first entity. The program instructions can also cause the processing component to predict, by the processor, the source of the subsequent spoken dialogue by employing, by the processor, a network that concurrently processes a transition type and a dialogue act. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a block diagram of an example, non-limiting system that can facilitate predicting a future source of dialogue in accordance with one or more embodiments described herein. 
         FIG.  2    illustrates an implementation of an example, non-limiting system that can facilitate predicting a future source of dialogue by employing an artificial neural network in accordance with one or more embodiments described herein. 
         FIG.  3    illustrates a process of optimizing a neural network used to facilitate predicting a future source of dialogue in accordance with one or more embodiments described herein. 
         FIG.  4    illustrates algorithms employed by a neural network after optimization to facilitate predicting a future source of dialogue in accordance with one or more embodiments described herein. 
         FIG.  5    illustrates example dialogue sources engaging in a spoken dialogue with one or more embodiments described herein predicting a future source of dialogue between the sources. 
         FIG.  6    depicts an example timeline that illustrates different features of dyadic dialogue structure that are analyzed in accordance with one or more embodiments described herein. 
         FIG.  7    depicts an example timeline that illustrates embodiments that can process human computer dialogue in accordance with one or more embodiments described herein. 
         FIG.  8    illustrates a flow diagram of an example, non-limiting computer-implemented method that can facilitate predicting a future source of dialogue in accordance with one or more embodiments described herein. 
         FIG.  9    depicts a flow diagram of an example, computer-implemented method that facilitates predicting a future source of dialogue in accordance with one or more embodiments described herein. 
         FIG.  10    illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section. 
     One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details. It is noted that the drawings of the present application are provided for illustrative purposes only and, as such, the drawings are not drawn to scale. 
     Speech processing can make interactions with spoken dialogue systems (e.g., a speaker using spoken I/O with a computer system) more natural and less rigid. Speech processing can also facilitate the parsing, by computer systems, of conversations between two or more speakers. Turn-management allows participants in a dialogue to exchange control of the floor, e.g., facilitating a determination of an order of dialogue of one or more participants. Conversation partners rely on many different types of cues to determine who will speak next, including lexical cues (e.g., meanings of spoken words: “So what do you think?”) and acoustic cues (e.g., pauses of different variations in a conversation). 
     Turn-taking in conversations can take many forms. The two basic turn-taking functions are turn-hold and turn-switch. Given an utterance in a conversation, a turn-hold indicates that the next utterance will be uttered by the same speaker while a turn-switch indicates that the next utterance will be uttered by the other speaker in the conversation. 
     One or more embodiments described herein can receive speech as an acoustic signal and, by analyzing the signal using different approaches, predict who the next speaker will be in a conversation, e.g., predict turn-holds and turn-switches. One or more embodiments can improve the accuracy and performance of detecting these events in dyadic spoken dialogues. Based on the features of one or more embodiments described herein, the process of automated dialogue management can be improved. 
       FIG.  1    illustrates a block diagram of an example, non-limiting system  150  that can facilitate predicting a future source of dialogue (e.g., the next speaker) in accordance with one or more embodiments described herein. 
     Speech receiving component  152  can receive first audio signal  125  and second audio signal  135  from first source  120  and second source  130  respectively. In one or more embodiments, first audio signal  125  can be a sequence of acoustic frames containing one or more utterances, e.g., uninterrupted chains of spoken language. 
     To facilitate processes, store results, and provide storage for processor executable components, system  150  accesses memory  165  and storage  170 , these respectively being, for example, Random Access Memory (RAM) and a hard drive. Processing component  160  can receive executable instructions from memory  165  and storage  170  and execute functional components, such as  152  and  153  discussed above. 
     As described further below, one or more embodiments can predict turn-switches by one or more approaches that can be utilized individually or in combination with other approaches. In an example, one or more embodiments can use an algorithm that predicts a turn-switch for an utterance based on characteristics of the utterance in an audio signal. Continuing this example, the one or more embodiments can also infer the intention of a speaker based on characteristics of the utterance. In this example, a combination of results of the two approaches can be used to yield a prediction of a turn-switch. Both of these approaches are discussed further below. 
     With respect to the first approach discussed above, different factors can provide indications of turn-switches or turn-holds in a conversation. For example, in one or more embodiments, it can be determined by analyzing acoustic cues that utterances with contours of intonation correlate with turn-transitions, while flat intonations correlate with turn-holds. 
     With respect to inferring speaker intentions discussed above, one or more embodiments can infer the intention of a speaker when they made an utterance. An intent to question, request, and make a statement can be termed a dialogue act in that these are utterances that a speaker intends to serve a function in a dialogue, e.g., from the other speaker: a question intends to seek information, a request intends to seek some action, and a statement seeks intends to convey information. In one or more embodiments, identifying which of the above intentions is present in a speaker at the time of an utterance can provide an indication as to who will speak next. 
     It should be noted that, as used herein, different unknown values can be described as being: identified, determined, inferred, and predicted. These terms should be considered to have equivalent meanings, and they are applied to events that will occur in the future (predict), occurred in the past (identify, determine), or are not knowable for sure at the moment (inferred). 
     An example of an utterance that can be indicative of different dialogue acts is: “There is a library nearby.” Interpreted in a purely lexical way, this utterance could be interpreted as at least three different dialogue acts, e.g.: a question to a listener asking whether a library is nearby, a request to a listener seeking directions to a nearby library, or a statement to a listener about a nearby library. In another example, the utterance “see you later,” in addition to being either a question, request, or statement, can also be a closing dialogue act, e.g., an utterance ending a conversation. In one or more embodiments, speaker identified dialogue acts can be good predictors of turn-taking behavior, e.g., in one or more embodiments, a switch in speaker turns is more likely to occur after encountering a question than it is to occur after encountering a statement. 
     An additional factor that can be considered by embodiments is the timing of dialogue act occurrence in a dialogue. Certain dialogue acts, when occurring at different relative times in an utterance (e.g., beginning, middle, or end) can be indicative of the intent of a speaker. For example, returning to the “there is a library nearby” utterance, if these words appear at the end of an utterance, it can increase the likelihood that the dialogue act is a question or a request. In another example, after a dialogue act has been identified by one or more embodiments, the time placement of the identified dialogue act can be relevant to other analyses, e.g., the likelihood of a turn-switch occurring, as discussed herein. 
     Returning to the components of  FIG.  1   , in one or more embodiments, because speaker intentions are not readily available at run-time, to improve results, speech processing component  153  can analyze an utterance to concurrently predict, both whether a turn-switch is indicated by the utterance, and what the intention of first source  120  was when the utterance was made, e.g., identifying whether a dialogue act was made, and if so, which act. As discussed further below, in one or more embodiments, by combining speaker intention and turn-switch indicator analysis, one or more embodiments can predict the next speaker in a dialogue better than either analysis performed separately. 
     One type of analysis that can be performed by one or more embodiments to infer speaker intentions and predict turn-switches can be the analysis of acoustic cues, these including, for example: intonation, pitch changes, speaking rate, and pauses occurring between utterances in a dialogue. In one or more embodiments, only the analysis of acoustic cues is be used to predict both speaker intentions and turn-switches from as little as one utterance. It is important to note that this example is non-limiting, and one or more embodiments can use combinations of different types of speech analysis for the two predictions. For example, the predictions can be performed by different combinations of analysis including, lexical analysis, syntactical analysis, analysis of acoustic cues, and other types of speech analysis. The acoustic cues only approach can be used by one or more embodiments based on performance and accuracy advantages, e.g., other analysis approaches can require more processor intensive processes. For example, lexical analysis can require use of speech recognition routines which are processor intensive compared to an analysis of acoustic cues only.  FIG.  2    describes different machine learning approaches that can be used to implement the above noted concurrent analyses. 
       FIG.  2    illustrates an implementation of an example, non-limiting system that can facilitate predicting a future source of dialogue by employing a neural network in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity. 
     This figure depicts first source  120  and second source  130 , speech receiving component  152 , and speech processing component  153  of  FIG.  1   , and further includes network component  280 , network optimizing component  260 , and optimizing dataset  270  in system  250 . In one or more embodiments, network component  280  is a neural network that can be optimized by network optimizing component  260  to perform the concurrent analyses of dialogue discussed herein. It should be noted that the description of employing a neural network included below is non-limiting, e.g., one or more embodiments can use other types of machine learning algorithms that receive input and make predictions. 
     One type of neural network that has characteristics that can benefit one or more embodiments described herein is a Multi-task (MT) neural network. For example, MT neural networks, unlike standard neural networks, can allow for the optimizing of both a primary task and one or more auxiliary tasks that can be related to the primary task. One advantage of this capability is that it allows the model to use representations that encode information about both tasks. For example, in one or more embodiments, logic that can infer speaker intention can also be encoded with the logic used to predict turn-switches and vice versa. 
     In one or more embodiments, the primary task of network component  280  can be predicting turn-transitions, and an auxiliary task can be inferring user intention. In example implementations, by handling these tasks in a MT neural network, embodiments can optimize the network component  280  for both tasks using the same optimizing dataset  270 , and thus improve the accuracy of the performance of both tasks. The optimizing and use of network component  280  are discussed with  FIGS.  3  and  4    below. 
     Because one or more embodiments model a sequence of acoustic features where past features can influence conclusions about current features, features of another type of neural network can be advantageously used with embodiments of the MT neural network discussed above. Generally speaking, Long Short-Term Memory (LSTM) networks, as used by one or more embodiments, are neural networks that can have loops in them, allowing some data to persist from one analysis to another. When one or more embodiments use an LSTM neural network to process sequences of acoustic frames over time, this persistence of some data can be advantageous. In addition to their ability to capture past signal behavior, LSTMs can capture information relating to timing and differentials, both of which can be used by one or more embodiments to model and predict turn-switching behavior.  FIG.  3    describes a process by which the neural networks used by one or more embodiments can be optimized for use. 
       FIG.  3    illustrates a process  300  of optimizing a neural network used to facilitate predicting a future source of dialogue in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity. 
     As included in  FIG.  3   , network optimizing component  340  can receive optimizing dataset  310  and, after processing, can use this data to optimize neural network  380 . In one or more embodiments, optimizing dataset  310  can include complete sentences, that contain both acoustic and lexical cues, and vary in duration. 
     One example dataset that can be used to optimize one or more embodiments comprises dyadic telephony conversations between participants who were asked to discuss various topics. The dataset conversations for analysis can include metadata that indicates the dialogue act types that were intended by utterances. This metadata can be used as ground truth labels for the optimization, as discussed below. For example, unlike rules-based systems, where specific relationships between values must be specifically defined, neural networks can use machine learning to learn combinations of features automatically, based on a datasets, including turn-switching labels  360 A and dialogue act labels  3 x 60 B. 
     In one or more embodiments, neural network  380  is a MT neural network that can concurrently process an utterance from optimizing dataset  310  to predict a turn-switch and infer a dialog act of the utterance. Illustrating this concurrent processing, in  FIG.  3   , network optimizing component  340  can receive optimizing dataset  310  for concurrent processing by both binary classifier  320  and dialogue act classifier  330 . 
     One or more embodiments can use dialogue act classifier  330  to classify dialogue acts in one or more dialogue act classes, e.g., statement, opinion, agree, abandon, backchannel, question, and answer. Binary classifier  320  can concurrently analyze the audio data and classify utterances as either indicating a turn-switch prediction or turn-hold prediction. Some algorithms that can be used by one or more embodiments are described below. 
     A dyadic dialogue can be represented, by one or more embodiments, as a sequence of utterances, taking the form of Equation Number 1, where each u i  is an utterance in the conversation: 
     
       
         
           
             
               
                 
                   
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     Based on the above, one or more embodiments use a function, spkr(⋅) that can return the speaker of a given utterance. Analyzing the utterance (u i ) can result in a prediction of whether the following Equation Number 2 is true or false: 
     
       
         
           
             
               
                 
                   
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     In one or more embodiments, if Equation Number 2 is true, then a turn-switch is predicted to take place with the other speaker speaking next. If Equation Number 2 is false, then a turn-hold is predicted to occur with current speaker continuing speaking. As discussed below, in one or more embodiments shown in  FIG.  3    both the results of binary classifier  320  and dialogue act classifier  330  can be reconciled to solve Equation Number 2 above. 
     In one or more embodiments, the results of binary classifier  320  and dialogue act classifier  330  can be reconciled by minimizing joint loss function  325 . This function can be a combination of two or more loss functions that use ground truth labels to identify and address potential errors in the model. 
     In an example, a dataset can have a sample conversation between two speakers, with one speaker saying “see you later.” Handling this utterance, an embodiment can detect question related acoustic cues, e.g., intonation, tone changes, etc. After evaluating all the available factors and weighing them based on previous learning, binary classifier  320  can predict a turn-switch based on intonation and dialogue act classifier  330  can infer a question dialogue act based on tonal cues. However, without metadata for this conversation that includes the actual intent of the speaker during the utterance (e.g., dialogue act labels  360 B), network optimizing component  340  could not either confirm or modify the logic that caused dialogue act classifier  330  to classify the utterance as a question. 
     In one or more embodiments, minimizing joint loss function  325  can take the form of Equation Number 3 below, where L turn  is the loss function for turn predictions, L intent  is the loss function for speaker intention predictions, L tot  is the overall loss function, and λ 1  and λ 2  can be weights selected to control the influence of each loss function in joint loss function  325 : 
     
       
         
           
             
               
                 
                   
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     In an example, weights can be assigned that reduce the influence of the intent loss function compared to the turn predictions loss function, e.g., λ 1 =1.0 and λ 2 =0.5. Using the results of joint loss function  325 , one or more embodiments can optimize the neural network models by optimizing the weighted negative log-likelihood loss function and further using a gradient descent optimizer. 
     It should be noted that the approaches described above (of optimizing a neural network with optimizing data, and subsequently using the optimized neural net) are non-limiting. One having skill in the relevant art(s), given the description herein would appreciate that the same concurrent processing of the same audio features can also be employed in a real-time system that does not require optimization before use. 
       FIG.  4    illustrates algorithms employed by a neural network after optimization to facilitate predicting a future source of dialogue in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity. 
     In one or more embodiments, as discussed with  FIG.  3    above, in one or more optimizing phases, neural networks can be optimized with datasets, then at run time the models can be used to provide results from input, e.g., by analyzing utterances to predict turn-switches in dyadic dialogue.  FIG.  4    depicts network  480  at run time, receiving a source utterance  410  from a speaker in a conversation with another speaker (not shown). 
     In one or more embodiments, at run time, network  480  is a MT neural network that can concurrently process source utterance  410  using binary classifier  482  and dialogue act classifier  485 , both of these being optimized using the processes described above. The concurrent processing, in one or more embodiments, can result in first prediction  430 A and second prediction  430 B. Prediction selector  490 , part of the logic of network  480 , can be optimized during the optimization phase, in one or more embodiments, to select one of the predictions for use as output from network  480 , given source utterance  410 . In the example shown in  FIG.  4   , second prediction  430 B can be selected, and this prediction can be either turn-hold  440 A or turn-switch  440 B. 
     As depicted in the embodiment of  FIG.  4   , although first prediction  430 A was not selected for network  480  output by prediction selector  490 , this prediction can still have value. In one or more embodiments, first prediction  430 A can be output to other speech recognition modules  402  for use as an element to other analyses. 
     It is to be appreciated that the functions of components of embodiments discussed herein (e.g., network  480 , network optimizing component  340 ) cannot be performed by a human, e.g., the functions performed are greater than the capability of a single human mind. For example, an amount of data processed, a speed of data processed or data types of data processed (e.g., hard to interpret acoustic cues) by one or more embodiments over a certain period of time can be greater, faster, and different than an amount, speed and data type that can be processed by a single human mind over the same period of time. 
       FIG.  5    illustrates example dialogue sources engaging in a spoken dialogue with one or more embodiments described herein predicting a future source of dialogue. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity. 
     This figure depicts first source  520  conducting a dialogue  525  with second source  530 , while being monitored, using approaches described by embodiments herein, by system  550 . In this example, as discussed with  FIG.  10    below, system  550  can be on a mobile device possessed by first source  520 , and the portability of this mobile device can facilitate system  550  supporting a variety of functions with respect to dialogue  525 , e.g., moderator, summarizer, opportunistic information provider, etc. The structure of a dialogue between two speakers, as analyzed by one or more embodiments, is discussed with  FIG.  6    below. 
       FIG.  5    also depicts third source  540  engaged in human computer dialogue (HCD)  555  with system  550 , using aspects of embodiments described herein with respect to two speaker dialogues, e.g., dialogue  525 . As described above, one or more embodiments can assess both turn-switches, and the intent of a speaker to perform different acts, e.g., dialogue acts. With respect to HCD dialogue, one or more embodiments can use these assessments to determine how best to respond to utterances of third source  540 . For example, the capacity of one or more embodiments to correctly respond to third source  540  stating “there is a library nearby” in accordance with the intent of third source  540  (e.g., a question, request for directions, or a statement), can improve the usefulness of the speech interface of system  550 . By correctly predicting whether third source  540  has completed speaking and whether a question was asked to system  550 , system  550  can respond faster and without requesting clarification from third source  540 . Additional examples of performance enhancements that can result from applying one or more embodiments to HCD  555  are discussed with  FIG.  7    below. 
       FIG.  6    depicts an example timeline that illustrates different features of dyadic dialogue structure that can be analyzed in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity. 
     Timeline  600  illustrates a dialogue  625  between first source  620  and second source  630 . The conversation begins with utterance U 1    640 A from first source  620 . In a notable event shown in  FIG.  6    that can be detected and analyzed by one or more embodiments, before first source  620  completes U 1    640 A, second source  630  begins speaking utterance U 2    670 . The simultaneous speaking of first source  620  and second source  630  is labeled as overlap  645 . 
     As can be assessed by one or more embodiments (e.g., by binary classifier  482 ), the acoustic cues from the overlapping speech can be identified and labeled as likely to cause a turn-switch. Moreover, as described above, concurrently with this analysis, one or more embodiments can analyze the acoustic cues of overlap  645  (e.g., by dialogue act classifier  485 ) and infer an interruption dialogue tag, e.g., second source  630  had a speaker intention to interrupt first source  620 . One or more embodiments can combine this dialogue tag inference with the turn-switch prediction noted above to yield a prediction that the next utterance will be from second source  630  and not the original speaker, second source  620 . Because the turn-switch occurs due to overlap  645 , one or more embodiments can identify this turn-switch as an overlapping switch. As shown in  FIG.  6   , the turn-switch after U 2    670  is a smooth switch, because there is silence (e.g., pause  660 , an acoustic cue) from the two speakers between two consecutive utterances. After U 3    640 B is completed, the conversation ends. 
       FIG.  7    depicts an example timeline  700  that illustrates embodiments processing human computer dialogue in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity. 
     As discussed with  FIG.  5    above (e.g., third source  540  engaging in HCD  555  with system  550 ), one or more embodiments can be used to predict turn-switches in dialogue between third source  720  and system  750 , for example, using some embodiments described herein to facilitate performance of another function, e.g., an assistant function. 
     HCD  725  of timeline  700  begins with request  740  from third source  720 . Based on acoustic cues, one or more embodiments can identify this utterance as a request for action dialogue act. When a computer is a second speaker in a dialogue, different considerations can be significant. One benefit that can be realized by one or more embodiments from improved turn-switch detection in this context can be an improvement in responsiveness and performance of the other function enabled by the speech analysis described herein, e.g., the assistant function noted above. 
     In an example assistant application, dialogue can occur in the form of request  740 , pause  760 , a turn-switch when request  740  is completed, processing  770  of request  740  by the assistant application, and answer  780  from the application. As discussed below, one area where one or more embodiments can improve performance is identified as latency  790 . 
     As shown in  FIG.  7   , latency  790  is the time interval between the ending of request  740  and the beginning of processing  770  of the completed utterance by system  750 . By definition, if system  750  is tasked with processing a completed request  740 , then before processing  770 , system  750  identifies that request  740  has been completed, e.g., that pause  760  is not a short pause during a longer request  740 . Latency  790  can occur beginning when request  740  ends and ending when system  750  identifies request  740  as having ended. 
     One or more embodiments, by using one or more of the features described herein (e.g., by analyzing acoustic cues not resource intensive lexical cues, and conducting concurrent analyses to predict turn-switch behavior and infer speaker intention) can improve the accuracy of system  750 , and reduce the time it takes to identify a turn-switch has commenced, thus reducing latency  790 . These examples are intended to be non-limiting, and one or more embodiments can be used for many types of applications. 
       FIG.  8    illustrates a flow diagram of an example, non-limiting computer-implemented method  800  that can facilitate predicting a source of a subsequent spoken dialogue in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity. 
     At  802  of the computer-implemented method  800 , a system (e.g., system  150 ) operatively coupled to a processor (e.g., processing component  160 ) can receive a spoken dialogue (e.g., first audio signal  125 ) from a first entity (e.g., first source  120 ). 
     At  804  of the computer-implemented method  800 , the system can predict the source of the subsequent spoken dialogue (e.g., first source  120  or second source  130 ) by employing, by the system, a network (e.g., network  480 ) that concurrently processes a transition type (e.g., binary classifier  482 ) and a dialogue act (e.g., dialogue act classifier  485 ). 
       FIG.  9    depicts a flow diagram of an example, non-limiting computer-implemented method  900  that can facilitate predicting a source of a subsequent spoken dialogue using an optimized multi-task network (e.g., network optimizing component  340  and neural network  380 ) in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity. 
     At  902  of the computer-implemented method  900 , a system operatively coupled to a processor can receive a spoken dialogue from a first entity. 
     At  904  of the computer-implemented method  900 , the system can predict the source of the subsequent spoken dialogue by employing the multi-task network that concurrently processes a transition type and a dialogue act. 
     At  906  of the computer-implemented method  900 , the system can optimize (e.g., network optimizing component  340 ) the multi-task network by employing a plurality of speech labels (e.g., turn switching labels  360 A and dialogue act labels  360 B) to predict the source of the subsequent spoken dialogue, and a joint loss function (e.g., joint loss function  325 ) to reconcile the transition type and the dialogue act. 
       FIG.  10    illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. For simplicity of explanation, the computer-implemented methodologies are depicted and described as a series of acts. It is to be understood and appreciated that the subject innovation is not limited by the acts illustrated and/or by the order of acts, for example acts can occur in various orders and/or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts can be required to implement the computer-implemented methodologies in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the computer-implemented methodologies could alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, it should be further appreciated that the computer-implemented methodologies disclosed hereinafter and throughout this specification are capable of being stored on an article of manufacture to facilitate transporting and transferring such computer-implemented methodologies to computers. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device or storage media. 
     In order to provide a context for the various aspects of the disclosed subject matter,  FIG.  10    as well as the following discussion are intended to provide a general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented.  FIG.  10    illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. Repetitive description of like elements and/or processes employed in other embodiments described herein is omitted for sake of brevity. 
     With reference to  FIG.  10   , a suitable operating environment  1000  for implementing various aspects of this disclosure can also include a computer  1012 . The computer  1012  can also include a processing unit  1014 , a system memory  1016 , and a system bus  1018 . The system bus  1018  couples system components including, but not limited to, the system memory  1016  to the processing unit  1014 . The processing unit  1014  can be any of various available processors. Dual microprocessors and other multiprocessor architectures also can be employed as the processing unit  1014 . The system bus  1018  can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and Small Computer Systems Interface (SCSI). 
     The system memory  1016  can also include volatile memory  1020  and nonvolatile memory  1022 . The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer  1012 , such as during start-up, is stored in nonvolatile memory  1022 . Computer  1012  can also include removable/non-removable, volatile/non-volatile computer storage media.  FIG.  10    illustrates, for example, a disk storage  1024 . Disk storage  1024  can also include, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memory stick. The disk storage  1024  also can include storage media separately or in combination with other storage media. To facilitate connection of the disk storage  1024  to the system bus  1018 , a removable or non-removable interface is typically used, such as interface  1026 .  FIG.  10    also depicts software that acts as an intermediary between users and the basic computer resources described in the suitable operating environment  1000 . Such software can also include, for example, an operating system  1028 . Operating system  1028 , which can be stored on disk storage  1024 , acts to control and allocate resources of the computer  1012 . 
     System applications  1030  take advantage of the management of resources by operating system  1028  through program modules  1032  and program data  1034 , e.g., stored either in system memory  1016  or on disk storage  1024 . It is to be appreciated that this disclosure can be implemented with various operating systems or combinations of operating systems. A user enters commands or information into the computer  1012  through input device(s)  1036 . Input devices  1036  include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices connect to the processing unit  1014  through the system bus  1018  via interface port(s)  1038 . Interface port(s)  1038  include, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). Output device(s)  1040  use some of the same type of ports as input device(s)  1036 . Thus, for example, a USB port can be used to provide input to computer  1012 , and to output information from computer  1012  to an output device  1040 . Output adapter  1042  is provided to illustrate that there are some output devices  1040  like monitors, speakers, and printers, among other output devices  1040 , which require special adapters. The output adapters  1042  include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device  1040  and the system bus  1018 . It should be noted that other devices and/or systems of devices provide both input and output capabilities such as remote computer(s)  1044 . 
     Computer  1012  can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s)  1044 . The remote computer(s)  1044  can be a computer, a server, a router, a network PC, a workstation, a microprocessor based appliance, a peer device or other common network node and the like, and typically can also include many or all of the elements described relative to computer  1012 . For purposes of brevity, only a memory storage device  1046  is illustrated with remote computer(s)  1044 . Remote computer(s)  1044  is logically connected to computer  1012  through a network interface  1048  and then physically connected via communication connection  1050 . Network interface  1048  encompasses wire and/or wireless communication networks such as local-area networks (LAN), wide-area networks (WAN), cellular networks, etc. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL). Communication connection(s)  1050  refers to the hardware/software employed to connect the network interface  1048  to the system bus  1018 . While communication connection  1050  is shown for illustrative clarity inside computer  1012 , it can also be external to computer  1012 . The hardware/software for connection to the network interface  1048  can also include, for exemplary purposes only, internal and external technologies such as, modems including regular telephone grade modems, cable modems and DSL modems, ISDN adapters, and Ethernet cards. 
     The present invention may be a system, a method, an apparatus and/or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium can also include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. Computer readable program instructions for carrying out operations of the present invention can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions can execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational acts to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the Figures. For example, two blocks shown in succession can, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     While the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer and/or computers, those skilled in the art will recognize that this disclosure also can or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks and/or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive computer-implemented methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments in which tasks are performed by remote processing devices that are linked through a communications network. However, some, if not all aspects of this disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     As used in this application, the terms “component,” “system,” “platform,” “interface,” and the like, can refer to and/or can include a computer-related entity or an entity related to an operational machine with one or more specific functionalities. The entities disclosed herein can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In another example, respective components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor. In such a case, the processor can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, wherein the electronic components can include a processor or other means to execute software or firmware that confers at least in part the functionality of the electronic components. In an aspect, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system. 
     In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Moreover, articles “a” and “an” as used in the subject specification and annexed drawings should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. As used herein, the terms “example” and/or “exemplary” are utilized to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as an “example” and/or “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. 
     As it is employed in the subject specification, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Further, processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches, and gates, in order to optimize space usage or enhance performance of user equipment. A processor can also be implemented as a combination of computing processing units. In this disclosure, terms such as “store,” “storage,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component are utilized to refer to “memory components,” entities embodied in a “memory,” or components comprising a memory. It is to be appreciated that memory and/or memory components described herein can be either volatile memory or nonvolatile memory or can include both volatile and nonvolatile memory. By way of illustration, and not limitation, nonvolatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or nonvolatile RAM (e.g., ferroelectric RAM (FeRAM). Volatile memory can include RAM, which can act as external cache memory, for example. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, the disclosed memory components of systems or computer-implemented methods herein are intended to include, without being limited to including, these and any other suitable types of memory. 
     What has been described above include mere examples of systems and computer-implemented methods. It is, of course, not possible to describe every conceivable combination of components or computer-implemented methods for purposes of describing this disclosure, but one of ordinary skill in the art can recognize that many further combinations and permutations of this disclosure are possible. Furthermore, to the extent that the terms “includes,” “has,” “possesses,” and the like are used in the detailed description, claims, appendices and drawings such terms are intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim. 
     The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.