Abstract:
Disclosed herein is a mobile face-to-face interaction monitoring device and method using the same and system including the same, for supporting accurate and efficient turn monitoring. One embodiment of the mobile face-to-face interaction monitoring device may comprise a conversation group detector for scanning mobile devices in a surrounding area and setting a conversation group, a turn detector for determining (conversational) turn using volume topography created based on sound signals detected in the mobile devices in the conversation group, and a meta-linguistic information processor for extracting meta-linguistic context of participants or interactants in the conversation group based on the turn. Other embodiments are described and shown.

Description:
BACKGROUND 
     1. Field of the Invention 
     The present disclosure generally relates to a mobile interaction- and context-aware device. More particularly, the present disclosure relates to a mobile face-to-face interaction monitoring device, interaction monitoring method using the same, interaction monitoring system including the same, and interaction monitoring applications executed on the same. 
     2. Description of Related Art 
     In a mobile and ubiquitous computing environment, where computing is made to be everywhere and anywhere, certain entities (e.g., participants or interactants) interact with each other in a dynamically changing context, for instance, in achieving a joint goal or task. For such interaction, the entities need to be aware of not only their physical context (e.g. location) but also their changing relationships with respect to the particular goal or task. Commonly referred to as social context, the latter also involves awareness, for example, of particular time period, setting, and on-going event or development. 
     Face-to-face interaction is an integral part of human life. Everyday, people dine with family, have meetings with colleagues, and spend time with friends. A promising new direction thus lies in monitoring (e.g., sensing, capturing, analyzing, and utilizing) social context during everyday face-to-face interactions. However, accurate and efficient monitoring of such social context in a mobile and ubiquitous environment remains a challenge as it is very difficult to realize without system-level support. 
     BRIEF SUMMARY 
     According to one embodiment, a mobile face-to-face interaction monitoring device may comprise a conversation group detector for scanning one or more mobile devices in a surrounding area and setting a conversation group, a turn detector for determining turn by using volume topography created based on sound signals detected in the mobile devices in the conversation group, and a meta-linguistic information processor for extracting meta-linguistic context of interactants in the conversation group the based on the turn. 
     According to another embodiment, the conversation group detector may further comprise a voice detector for detecting sound signals and an interactant manager for storing mobile device addresses and forming the conversation group when the mobile devices having the addresses are scanned. 
     According to another embodiment, the conversation group detector may further comprise a subgroup detector for determining subgroup formation by analyzing overlapping speech pattern in the sound signals. 
     According to another embodiment, the conversation group detector may further comprise a head selector for selecting a head mobile device among a plurality of mobile devices in the conversation group, wherein the head mobile device collects the sound signals in the mobile devices in the conversation group, determines the turn, and transmits the turn to the mobile devices. 
     According to another embodiment, the mobile face-to-face interaction monitoring device may further comprise a monitoring planner for controlling monitoring of the turn based on information of the conversation group. 
     According to another embodiment, the monitoring planner may comprise a source selector for determining a quantity of the mobile devices in the conversation group and selecting one or more source mobile devices for collaborative turn monitoring among the mobile devices in the conversation group, and an execution planner for controlling the monitoring in the turn detector. 
     According to another embodiment, the monitoring planner may further comprise a sync manager for synchronizing the mobile devices in the conversation group. 
     According to another embodiment, the monitoring planner may further comprise a resource monitor for monitoring resources and resource utilization in the mobile devices. 
     According to another embodiment, the turn detector may comprise a volume topography builder for creating the volume topography based on the sound signals in the mobile devices in the conversation group, during a training period; and a volume topography matcher for determining current turn by matching current sound signals with the volume topography, after the training period. 
     According to another embodiment, the turn may have factors of speaker, start time, and end time. 
     According to another embodiment, the meta-linguistic information processor may comprise a feature extractor for extracting prosodic features of the sound signals and a pattern analyzer for determining interaction pattern among the interactants in the conversation group, based on the turn and at least one of the prosodic features. 
     According to another embodiments, the pattern analyzer may determine interactivity based on a quantity of the turn in a given time, sparseness based on a quantity of non-speech turn in a given time, and skewness based on a standard deviation of a quantity of the turn for all of the interactants, 
     According to another embodiment, the mobile face-to-face interaction monitoring device may further comprise an interaction history manager for storing information of the turn and a conversation session and supporting user query and generating an interaction history based on the information. 
     According to an embodiment, a mobile face-to-face interaction monitoring method may comprise: scanning one or more mobile devices in a surrounding area and setting a conversation group; detecting sound signals in the mobile devices in the conversation group; creating volume topography based on the sound signals; determining turn by using the volume topography; and extracting meta-linguistic context of interactants in the conversation group based on the turn. 
     According to another embodiment, the mobile face-to-face interaction monitoring method may further comprise: determining a quantity of the mobile devices in the conversation group; and selecting one or more source devices among the mobile devices in the conversation group, for collaborative turn monitoring among the mobile devices in the conversation group. 
     According to another embodiment, the mobile face-to-face interaction monitoring method may further comprise synchronizing the mobile devices in the conversation group. 
     According to another embodiment, the volume topography may be created based on the sound signals during a training period. 
     According to another embodiment, the turn may be determined by using the volume topography by determining current turn by matching current sound signals with the volume topography, after the training period. 
     According to another embodiment, the volume topography may be created by using a feature vector P(t) defined as P(t)=(p(t,1), p(t,2), . . . , p(t,np)), where p(t, i) is an average of a square of the sound signals in each mobile device i at a given time t, and where np is a quantity of the mobile devices in the conversation group. 
     According to another embodiment, the volume topography may be created by using a normalized vector P′(t) defined as P′(t)=P(t)/E(t), where E(t) is an average of the feature vector P(t). 
     According to another embodiment, the volume topography may be created by using a transformed vector P″(t) defined as
 
 P ″( t )= D ( t )* P ′( t )={ D ( t, 1)* p ( t, 1)/ E ( t ), D ( t, 2)* p ( t, 2)/ E ( t ), . . . , D ( t,np )* p ( t,np )/ E ( t )},
 
where a decibel D(t) measured on the mobile device i, at the given time t, is defined as D(t,i)=20*log 10(p(t,i)/p.ref), where p.ref is a standard reference sound pressure level.
 
     According to another embodiment, the quantity np of the mobile devices may be equal to or less than a number of the interactants in the conversation group. 
     According to another embodiment, a quantity of n+l of the volume topography may be created, where n is a number of the interactants in the conversation group. Each of the n-quantity of the volume topography created may respectively correspond to each of the n-number of the interactants, and the l-quantity of the volume topography created may correspond to non-speech in the conversation group. 
     According to another embodiment, the mobile face-to-face interaction monitoring method may further comprise recreating the volume topography when there is a change in the conversation group and the surrounding area. 
     According to another embodiment, the meta-linguistic context of interactants in the conversation group may be extracted based on the turn by extracting features of the turn; extracting prosodic features of the sound signals; and determining interaction pattern among the interactants in the conversation group, based on the features of the turn and at least one of the prosodic features. 
     According to one or more embodiments, the features of the turn may be extracted by determining the features of the turn categorized as: individual for each of the interactants, relational among the interactants, and cumulative for one or more conversation sessions and the conversation group. 
     According to an embodiment, the individual features of the turn may include speaking time, a quantity and duration of speech and non-speech turns, and associated statistics, for each of the interactants. 
     According to an embodiment, the relational features of the turn may include orders and frequencies of exchanges of the turns and ratio thereof, among the interactants in the conversation group. 
     According to an embodiment, the cumulative features of the turn may include a number of the interactants in the conversation sessions, a quantity and duration of the conversation sessions, and a quantity and duration of the turns in the conversation sessions and the conversation group. 
     According to an embodiment, an interaction monitoring system may comprise a first mobile device, which determines the turn, and at least one second mobile device, which receives the turn from the first mobile device. 
     According to an embodiment, the first mobile device may comprise a first conversation group detector for scanning one or more mobile devices in a surrounding area and setting a conversation group, a turn detector for determining turn by using volume topography created based on sound signals detected in the mobile devices, and a first meta-linguistic information processor for extracting meta-linguistic context of interactants in the conversation group the based on the turn. 
     According to an embodiment, the second mobile device may comprise a second conversation group detector for scanning the mobile devices in the surrounding area and setting the conversation group, and a second meta-linguistic information processor for extracting the meta-linguistic context of the interactants in the conversation group based on the turn received from the first mobile device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the accompanying drawings, closely related figures (FIG.&#39;s) have the same number but different alphabetical suffixes. 
         FIG. 1  is a block diagram showing a mobile interaction monitoring device according to an embodiment. 
         FIG. 2  is a high-level process diagram showing interaction monitoring (operation) in the mobile device according to an embodiment. 
         FIG. 3  is a diagram showing API&#39;s in the mobile device according to an embodiment. 
         FIG. 4  is a diagram showing a conversation session (structure) for the API&#39;s in the mobile device according to an embodiment. 
         FIG. 5  is a diagram showing turn information for the API&#39;s in the mobile device according to an embodiment. 
         FIG. 6  is a diagram showing turn monitoring (operation) in the mobile device according to an embodiment. 
         FIGS. 7A-7E  are graphs showing distribution(s) of feature vectors which may be used in turn monitoring (operation) in the mobile device according to an embodiment. 
         FIGS. 8A-8C  are diagrams showing mobile interaction monitoring applications the mobile device according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a block diagram showing a mobile interaction monitoring device, according to an embodiment.  FIG. 2  is a high-level operation diagram showing interaction monitoring in the mobile device in  FIG. 1 , according to an embodiment. 
     Referring to  FIG. 1  and  FIG. 2 , the mobile interaction monitoring device may include a conversation group detector  100 , a turn detector  300 , and a meta-linguistic information processor  400 . The mobile device may further include a monitoring planner  200 . The mobile device may further include an interaction history manager  500 . 
     The mobile device may be one of a smartphone, tablet PC, PDA, and laptop computer. However, the mobile device is not limited to or dependent on such device types. 
     The mobile device may include a plurality of Application Program Interfaces (API&#39;s). Various interaction monitoring applications may be executed and operate in the mobile device, which includes the API&#39;s. Examples of the API&#39;s are described in detail with references to  FIG. 3  through  FIG. 5 . Examples of the interaction monitoring applications are described in detail with references to  FIGS. 8A-8C . 
     The conversation group detector  100  scans one or more mobile devices, for example, belonging to participants or interactants, in a given perimeter or a general surrounding area and sets a conversation group. The conversation group detector  100  may include a voice detector  110 , a subgroup detector  120 , an interactant manager  130 , and a head selector  140 . 
     The voice detector  110  detects a human voice or sound signals. When sound signals are detected in a surrounding area, a conversation group may be formed by the conversation group detector  100 . (Even) when mobile devices are scanned in a surrounding area, a conversation group may not be formed, for example, when the mobile devices are merely passing by or when a conversation is not on-going. According to one embodiment, the sound signals may be filtered or pre-filtered of the sound signals&#39; non-human vocal spectrum and point-sourced sound or noise, which may distort the volume topography. 
     The mobile device scans and detects other mobile devices in a surrounding area via network interface. The mobile device may scan other mobile devices, for example, using Bluetooth technology. 
     The interactant manager  130  may include a database, which stores addresses of other mobile devices. For example, the interactant manager  130  may store MAC addresses of (other) mobile devices belonging to family and friends. The interactant manager  130  may form a conversation group when the mobile devices belonging to family and friends are scanned and detected. 
     The head selector  140  may select a head mobile device among a plurality of mobile devices in the conversation group. The head mobile device may perform turn monitoring and determine a (conversational) turn. The head mobile device may, for example, be randomly selected. According to one embodiment, the head selector  140  may be included in the monitoring planner  200 . 
     The head mobile device may coordinate collaborative turn monitoring by the plurality of mobile devices in the conversation group. The head mobile device may collect the sound signals from the plurality of mobile devices in the conversation group and determine the turn. For example, other devices (e.g., non-head mobile devices) in the plurality of mobile devices may not determine the turn. The head mobile device may output or transmit the turn (e.g., information related to the turn) to the other devices in the plurality of mobile devices in the conversation group. 
     The subgroup detector  120  may determine subgroup formation by analyzing overlapping speech patterns in the sound signals detected in the plurality of mobile devices in the conversation group. A given conversation group may be further divided into a plurality of subgroups. Generally, in a single conversation-group situation, interactants&#39; speech patterns do not overlap as the interactants, for example, speak one at a time. However, in a subdivided conversation-group situation (e.g., with subgroups), interactants&#39; speech patterns, for example, in one subgroup, may overlap with speech patterns in another subgroup. Therefore, the subgroup detector  120  may divide the conversation group into a plurality of subgroups, based on overlapping speech patterns. 
     The monitoring planner  200  controls monitoring of the turn based on information related to the conversation group (e.g., provided by the conversation group detector  100 ). The monitoring planner  200  may include an execution planner  210 , a source selector  220 , a sync manager  230 , and a resource monitor  240 . 
     The execution planner  210  controls monitoring (operation) in the turn detector  300 . The execution planner  210  may also perform conventional speaker or voice recognition. 
     The source selector  220  determines a quantity of the plurality of mobile devices in the conversation group and selects one or more source mobile device(s) among the plurality of mobiles devices in the conversation group for collaborative turn monitoring. The source selector  220  may, for example, check whether the mobile devices in the conversation group have sufficient power (e.g., battery levels) and whether the sound signals have sufficient clarity for discriminative volume topography. The source selector  220  may, for example, exclude as a source mobile device a mobile device with a low power level (e.g., less than 10% of battery power remaining) or a mobile device with a high noise level for increasing accuracy of the turn monitoring. 
     The sync manager  230  synchronizes the plurality of mobile devices in the conversation group. As the volume topography involves feature vectors, which has a time factor, time elements (e.g., clocks) in the plurality of mobile devices are aligned for increasing accuracy of the turn monitoring. 
     The resource monitor  240  monitors resources and resource utilization in the mobile devices, such as power levels and consumption, CPU utilization, and bandwidth states. The resource monitor  240  may monitor the resources and utilization in real-time and transmit information related to the resources and utilization to other mobile devices in the conversation group. 
     The turn detector  300  monitors and determines the turn using volume topography (database) generated based on the sound signals detected in the plurality of mobile devices in the conversation group. Generally in conversation analysis, the (conversational) turn is a continuous speech segment where a speaker starts and ends the speaker&#39;s speech. For example, the turn may indicate which one of the interactants in the conversation group is the speaker (speaking). The turn may have factors such as speaker (identity), start time, and end time and include the factors, for example, as turn information. 
     The turn detector  300  may include a volume topography builder  310 , a volume topography matcher  320 , and a speaker recognizer  330 . 
     The volume topography builder  310  creates the volume topography based on the sound signals in the plurality of mobile devices in the conversation group. The volume topography may be created, for example, during a learning or training period. According to one embodiment, the volume topography builder  310  may re-train or recreate the volume topography to instantaneously incorporate or reflect any change in the conversation group and other dynamic events in the surrounding area. 
     The volume topography matcher  320  determines current turn by analyzing and matching current (incoming) sound signals with the volume topography, for example, after the training period. 
     The speaker recognizer  330  may generate a reference speech model of each of the interactants (e.g., owners of the mobile devices) and store the reference speech model in a database. Based on the reference speech model, the speaker recognizer  330  may identify and assign audio signatures (cluster-ID&#39;s) of the conversation group as/to voice or sound of the interactants (member-ID&#39;s) of the conversation group. Each of the mobile devices may, for example, generate MFCC (Mel-Frequency Cepstral Coefficient) and compute GMM&#39;s (Gaussian Mixture Models) based on the audio signatures of the owners of the mobile devices. 
     The monitoring (operation) in the turn detector  300  is described in detail with references to  FIG. 6  and  FIGS. 7A-7E . 
     The meta-linguistic information processor  400  monitors meta-linguistic interactions of the interactants in the conversation group and extracts or determines meta-linguistic context of the interactants based on the turn (information). 
     The meta-linguistic information processor  400  may determine and extract turn features from the turn. Referring to  FIG. 2 , the turn features may be categorized as features of the turn that are associated with an individual interactant, relationships among the interactants, and an entire interaction. The turn features associated with the individual interactant in the conversation group may, for example, include speaking time, a quantity and duration of speech and non-speech turns, and associated statistics. The turn features associated with the relationships among the interactants may, for example, include turn-taking orders, turn-taking frequencies, and a ratio of the turns between any two or (among) more of the interactants in the conversation group. The turn features associated with the entire interaction may, for example, include a number of the interactants in a conversation session, duration of one or more of the conversation session(s), and duration of the speech and non-speech turns in/for the conversation session(s) and the conversation group. 
     The meta-linguistic information processor  400  may include a feature extractor  410  and a pattern analyzer  420 . 
     The feature extractor  410  extracts and processes prosodic features of the sound signals. The feature extractor  410  may, for example, determine volume or pitch of the sound signals and process means, variances, and distributions of the volume or the pitch. The feature extractor  410  may also, for example, determine rhythm, formants, bandwidth, and spectral intensity of the sound signals. 
     The pattern analyzer  420  determines interaction pattern or context among the interactants in the conversation group, based on the turn (information) or the turn features and at least one of the prosodic features (processed by the feature extractor  410 ). The pattern analyzer  420  may, for example, determine the interaction pattern or context based on the turn features complemented by at least one of the prosodic features. 
     For example, the pattern analyzer  420  may determine a pace of a conversation (session) based on the duration of the turn(s) of the interactants in the conversation group. The conversation session may, for example, be determined based on the start and end times of the turns. 
     For example, the pattern analyzer  420  may determine dominance and leadership (of the interactants) in the conversation group. The dominance may be determined based on the ratio of the turn(s) of or taken by the interactants in the conversation group. The leadership may be determined based on the ratio of the turn(s), and successive or interrupting turn characteristics of the interactants in the conversation group. Based on the dominance and leadership, the meta-linguistic information processor  400  may determines other meta-linguistic context of the interactants, for example, as to roles and role-playing patterns of the interactants in the conversation group. 
     For example, the pattern analyzer  420  may also determine conversational interactivity, sparseness, and skewness or asymmetry of the interactants in the conversation group. The interactivity may be determined based on the quantity of the turn(s) in a given time. The sparseness may be determined based on the quantity of non-speech (e.g., silence) turn(s) in a given time or over a given interval in a given time. The skewness may be determined based on a standard deviation of the quantity of the turn(s) for all of the interactants in the conversation group. 
     The interaction history manager  500  stores information related to the turn and the conversation session (e.g., associated with the turn). The interaction history manager  500  may support user query and generate an interaction report or history based, for example, on the information. The interaction history manager  500  may include a database for storing the information. 
     Examples of the user queries may be: “How many turns has John had in the last 10 minutes?” or “Who are the 3 friends with whom John has had most conversation sessions?” 
       FIG. 3  is a diagram showing examples of the API&#39;s in the mobile device in  FIG. 1 .  FIG. 4  is a diagram showing an example of the conversation session (structure) for the API&#39;s in  FIG. 3 .  FIG. 5  is a diagram showing an example of the turn information for the API&#39;s in  FIG. 3 . 
     Referring to  FIG. 1  through  FIG. 5 , the API&#39;s in the mobile device may be classified as monitoring of the conversation session and turn, monitoring of the prosodic features and meta-linguistic interaction characteristics, and querying of the interaction history. 
     The API for the monitoring of the conversation session and turn may, for example, be registerSessionStartListener(callback(Session), conditions) for detecting (or tracing) a beginning of a session and registerTurnChangeListener(callback(Turn)) for detecting (or tracing) a turn change. The conditions for the registerSessionStartListner may be designated persons (TARGET_PERSON) or places (TARGET_PLACE). The registerSessionStartListner and the registerTurnChangeListner may detect (or trace) a beginning of the (conversation) session and the turn change, respectively, by using a callback function. 
     The API for the monitoring of the prosodic features and meta-linguistic interaction characteristics may, for example, be enableProsodicFeature(session_id, features) for retrieving the prosodic features (information) associated with the turn; and getSparsity, getInteractivity, getAsymmetry, registerDominanceListener, and registerLeadershipListner. For example, getSparsity determines how far the turn(s) are separated by the non-speech turn(s). For example, registerDominanceListener determines who among the interactants has dominance in the conversation session or in the conversation group. 
     The API for the querying of the interaction history may, for example, be getOnGoingSessionHistory(“SQL_Query_Statement”) for querying an on-going conversation session for current interaction history and getPastInteractionHistory(“SQL_Query_Statement”) for querying a past conversation session for previous interaction history. 
     Referring to  FIG. 4 , information for the conversation session may, for example, include a session identification (sID), interactants (Interactants) participating in a session, a starting and ending time(s) of a session (start_time, end_time, respectively), and a location of a session (place). 
     Referring to  FIG. 5 , the turn information may, for example, include a session identification (sID), a turn identification (tID), a speaker associated with a turn (speaker), a starting and ending time(s) of a turn (start_time, end_time, respectively), and a prosodic pointer (prosodic_ptr) to a prosodic features table. 
       FIG. 6  is a diagram showing the turn monitoring (operation) in the mobile device in  FIG. 1 , according to an embodiment.  FIGS. 7A-7E  are graphs showing distribution of feature vectors, which may be used in the turn monitoring in the mobile device in  FIG. 1 . 
     Referring to  FIG. 1  through  FIGS. 7A-7E , and considering a three-interactant conversation group scenario, with one interactant speaking (i.e., Interactant A or Speaker A, among Interactants A, B, and C) as in  FIG. 6 , a plurality of mobile devices may detect and capture, for example, through wireless receivers, sound (or voice) signals generated by the speaker (or a transmitter). Each mobile device may detect and measure strength (or volume) of sound signals generated by the speaker. A mobile device that is in close proximity to the speaker (e.g., her own phone) may detect and measure strength of the sound signals as being high (or highest), for example, relative to strength detected and measured by other mobile devices. A volume peak method may use this high strength or peak characteristic to make speaker recognition (e.g., determine who is speaking). 
     The volume peak method, however, has limitations. For example, location or placement of the mobile devices may not be controllable (e.g., a phone may be placed in a pocket). Some of the mobile devices may be rendered unsuitable or unusable, for example, due to a low power level or poor recording quality. And peak detection may not be accurate in an environment with background noise. 
     To overcome these and other limitations, a volume topography method, according to an embodiment, uses relative difference of sound signals recorded in the plurality of mobile devices. 
     Referring to  FIG. 6 , a sound signal or signals generated by the speaker (Interactant A) are recorded in different volumes (e.g., represented as a volume vector), for example, by three mobile devices (or phones). Due to relative differences in positions of the mobile devices, each interactant has unique or discriminative volume signature or volume topography (e.g., over the three mobile devices). The mobile devices collaboratively build a volume topography (database) (e.g., during the training period) and perform speaker (turn) recognition and (the) turn monitoring by analyzing and matching a newly measured volume vector or vectors with the volume topography (database) (e.g., after the training period). 
     The volume topography method, according to the embodiment, is much lighter than conventional speaker recognition methods, as relatively complex signal processing may be substantially limited to the training period. The volume topography method (and the mobile device and system using the method) are resource- and energy-efficient as the method uses much simplified computation. The volume topography method provides agile turn monitoring (e.g., with speed and reliability), as a very short sensing window (e.g., 0.3 seconds) may be used. Also, the volume topography method provides accurate speaker recognition (even) in an environment with background noise, as a sound signal (despite) containing background noise may be consistently matched with volume topography (also containing the background noise). 
     Each of the mobile devices may sample the sound signals, for example, during the training period. The sound signals may be sampled, for example, a rate of 8 kHz. An audio stream (of the sound signals sampled) may be segmented, for example, into 300 ms-frames (2400 samples). At or for a given time “t” and each mobile device “i” (of the mobile devices), a power (of the frame) of the mobile device “i,” at time “t,” may be calculated as p(t,i), which may be an average of a square of the sound signals. A feature vector P(t) may be defined as {p(t,1), p(t,2), . . . , p(t,np)}, where “np” is a quantity of the mobile devices. The quantity “np” may be equal to or less than a number of the interactants in the conversation group. For example, the quantity “np” may be a quantity of the mobile devices participating in the (collaborative) turn monitoring in the conversation group. The mobile devices may collect the feature vector P(t) for L seconds, where L is a parameter for the training period. The parameter L may, for example, be 60 (seconds). For the 300 ms-frames, for example, 200 of the feature vector P(t) may be collected in L=60 (seconds). 
     For accuracy of the turn monitoring, for example, the volume topography may be created based on the feature vector P(t). 
       FIG. 7A  is a graph showing a distribution of the feature vector P(t) for a conversation group with three interactants with three mobile devices. 
     For increasing the accuracy of the turn monitoring, the feature vector P(t) may be defined or transformed for discriminative power. 
     For example, the volume topography may be created based on a normalized feature vector P′(t), which may be a quotient of the feature vector P(t) and E(t), where E(t) is an average of the feature vector P(t): P′(t)=P(t)/E(t)={p(t,1)/E(t), p(t,2)/E(t), . . . , p(t,np)/E(t). 
       FIG. 7B  is a graph showing a distribution of the normalized feature vector P′(t) for the conversation group with three interactants with three mobile devices. In  FIGS. 7A-7E , “NS” indicates a cluster of (the) non-speech or silent turn(s). The volume topography based on the normalized feature vector P′(t) may be used to (relatively more) accurately determine and distinguish the speech and non-speech turn (for each) of the interactants.  FIG. 7C  is a graph showing a distribution of the normalized feature vector P′(t) for a conversation group with three interactants (but) with two mobile devices. 
     For example, the volume topography may also be created based on a transformed vector P″(t), which may be a product of the normalized feature vector P′(t) and a decibel measured on the mobile device “i”: P″(t)=D(t)*P′(t)={D(t,1)*p(t,1)/E(t), D(t,2)*p(t,2)/E(t), . . . , D(t,np)*p(t,np)/E(t)}, where the decibel is defined as D(t,i)=20*log 10(p(t,i)/p.ref), and where p.ref is a standard reference sound pressure level. For example, the standard reference “p.ref” may be 20 μPa. 
       FIG. 7D  is a graph showing a distribution of the transformed vector P″(t) for the conversation group with three interactants with three mobile devices.  FIG. 7E  is a graph showing a distribution of the transformed feature vector P″(t) for the conversation group with three interactants (but) with two mobile devices. The volume topography based on the transformed feature vector P″(t) may be used to (relatively more) accurately determine and distinguish the speech and non-speech turn (for each) of the interactants, where the quantity “np” or the quantity of the mobile devices participating in the (collaborative) turn monitoring in the conversation group may be less than the number of the interactants in the conversation group. 
     Based on a dataset collected, for example, during the training period, the volume topography (e.g., represented as a set of audio (signal) signatures) may be created (for a speech case) for each of the interactants in the conversation group. The volume topography may be created for a silent or non-speech case in the conversation group, for example, when none of the interactants speaks. The volume topography may also be created for the silent or non-speech case for all of the interactants in the conversation group. 
     For example, for “n” number of the interactants in the conversation group, a total “n+l” quantity of the volume topography may be created. Each volume topography in the “n” quantity of the volume topography(s) may correspond to or be associated with each interactant of the “n” interactants in the conversation group; the “l” volume topography may be associated with or characterized by the non-speech case in the conversation group. 
     According to one embodiment, the volume topography may be created before the training period, for example, by applying conventional method (e.g., the volume peak method) in parallel. 
     According to another embodiment, the volume topography may be re-trained or recreated to instantaneously incorporate or reflect any change in the conversation group and other dynamic events in the surrounding area, for example, during the learning period. The change may be based on various sources: e.g., addition (joining) or deletion (leaving) of new or existing interactants and erratic turn (taking) patterns by the interactants. The volume topography may be re-trained while a prior version of the volume topography is being used. 
     In the dataset collected, for example, during training period, the feature vectors are labeled with cluster-ID&#39;s. The turn may be determined simply by mapping incoming frames (of the audio stream of the sound signals sampled) into the cluster-ID&#39;s, for example, after the training period. The mapping may be performed, for example, by an SVM classifier. 
     The turn may be detected and determined, for example, when two consecutive frames (in the audio stream) belong to different clusters. According to one embodiment, the non-speech turn, which may be less than 300 ms, may not be considered as the non-speech turn may, for example, be a small pause. 
     A mapping table may be created to convert the audio signatures or cluster-ID&#39;s to member-ID&#39;s for each of the interactants (or member) in the conversation group, for example, during the training period. Each mobile device may train speaker or voice recognition algorithm for the interactant (e.g., to whom each mobile device belongs). the mobile device may use all of the frames in each of the audio signatures to generate MFCC&#39;s and compute GMM&#39;s, for example, after the training period. A head cluster among the clusters may collect GMM&#39;s from each of the interactants and perform mapping of the cluster-ID&#39;s to the member-ID&#39;s. 
     According to one or more embodiments, the training period may be relatively very short (e.g., 30-60 seconds) in duration, and thus, the turn monitoring (operation) may commence immediately with a beginning of a conversation (session). 
     As a rate of sound signal sampling in the turn detector  300  increases, accuracy of the turn monitoring may increase; and as the rate of sampling decreases, the accuracy of the turn monitoring may decrease. According to one embodiment, the turn detector  300  may perform the turn monitoring with sufficiently high accuracy at a low rate of sampling of 500 Hz. Thus, the rate of sampling rate in the turn detector  300  may, for example, be 500 Hz. According to another embodiment, a user&#39;s (e.g., the interactant&#39;s) privacy may be protected as the sound signals may, for example, be sampled at a very low rate (e.g., 500 Hz). 
     An interaction monitoring system, according to an embodiment, may include a plurality of mobile devices. For example, the interaction monitoring system may include a first mobile device, which primarily determines the turn, and a second mobile device, which receives the turn from the first mobile device. The first mobile device may be a head mobile device; the second mobile device may be a guest mobile device. For example, the interaction monitoring system may include a plurality of guest mobile devices. 
     During the conversation session, the head mobile device may be changed from the first mobile device to the second mobile device. The second mobile device may then operate as the head mobile device, and the first mobile device may then operate as the guest mobile device. 
     The head mobile device may perform (each respective) operations of the conversation group detector  100 , the monitoring planner  200 , the turn detector  300 , the meta-linguistic information processor  400 , and the interaction history manager  500 , in  FIG. 1 . 
     The guest mobile device may perform (each respective) operations of the conversation group detector  100 , the meta-linguistic information processor  400 , and the interaction history manager  500 , in  FIG. 1 . 
     The head mobile device may be selected by the head selector  140  of the conversation group detector  100 . The head mobile device may coordinate collaborative turn monitoring by the plurality of mobile devices in the conversation group. The head mobile device may create volume topography of its own and collect volume features (e.g., different sound signal strength, unique volume-signatures information) and volume topography from (each of) the guest mobile devices. The head mobile device may sample sound signals and monitor and determine the turn by matching the sound signals with the volume topographies. The head mobile device may output or transmit the turn (e.g., the turn information in  FIG. 5 ) to the guest mobile devices in the conversation group. 
     The meta-linguistic information processor  400  in the head mobile device extracts or determines meta-linguistic context of the interactants in the conversation group based on the turn determined by the turn detector  300 . 
     The meta-linguistic information processor  400  in the guest mobile device extracts or determines the meta-linguistic context of the interactants in the conversation group based on the turn determined by the head mobile device. 
     According to one or more embodiment(s), the volume topography may be instantly and reliably created and thus, a highly agile turn monitoring may be performed. For example, the turn monitoring may be performed in much shorter time. According to one or more embodiment(s), the volume topography may be quickly retrained and/or recreated in-situ to instantaneously to incorporate or reflect any change in the conversation group and other dynamic events in the surrounding area: e.g., positions of the mobile devices, addition (joining) or deletion (leaving) of new or existing interactants, and background noise or other sound characteristics. Thus, accurate turn monitoring may be performed in various environments. 
       FIGS. 8A-8C  are diagrams showing examples of mobile interaction monitoring application in the mobile device in  FIG. 1 . 
     Referring to  FIG. 1  through  FIGS. 8A-8C , a plurality of mobile interaction monitoring applications may be executed in the mobile device in  FIG. 1 . The mobile applications may generate a detailed report of a face-to-face interaction. 
     The mobile application may scan one or more mobile devices, for example, belonging to the interactants in the given perimeter or the general surrounding area and set the conversation group. The mobile application may (then) create the volume topography based on the sound signals detected in the mobile devices in the conversation group and determine the turn based on the volume topography. Based on the turn, the mobile application may determine and generate a detailed report of the conversation group, the interactants in the conversation group, and the conversation sessions. 
     The mobile application may determine the features of the turn, for example, categorized as: (i) individual for each of the interactants (e.g., the speaking time, the quantity and duration of the speech and non-speech turns, and the associated statistics); (ii) relational or relative among the interactants (e.g., the turn-taking orders, pair-wise turn-taking frequencies, the ratio of the turns among the interactants); and (iii) sessional or cumulative for the conversation session and for the conversation group (e.g., the number of the interactants in the conversation session, the duration of the conversation session, the duration of the speech and non-speech turns for the conversation session and for the conversation group). 
     Referring to  FIG. 8A , the mobile application may, for example, determine a (total) number of the interactants and cumulative conversation time for each of the interactants, with whom a user (e.g., a given interactant in the conversation group) has had a conversation session. Referring to  FIGS. 8B-8C , the mobile application may, for example, determine relative conversation time (e.g., the speaking time) and a relative and cumulative quantity of the turn and turn exchanges (e.g., the turn-takings), and a ratio thereof, for each of the interactants in the conversation session. The mobile application may, for example, generate and display suggestive system remarks relating to the conversation session for the user. 
     Exemplary embodiments have been described in detail with references to the accompanying drawings, for illustrative purposes (and) to solve technical problems. Although the description above contains many specificities, these should not be construed as limiting the scope of the exemplary embodiments. The exemplary embodiments may be modified and implemented in various forms and should not be interpreted as thus limited. 
     Persons skilled in the art will understand that various modifications and alterations may be made without departing from the spirit and scope of the description and that such modifications and alterations are within the scope of the accompanying claims.