Patent Publication Number: US-7716048-B2

Title: Method and apparatus for segmentation of audio interactions

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to audio analysis in general and to a method and apparatus for segmenting an audio interaction, in particular. 
     2. Discussion of the Related Art 
     Audio analysis refers to the extraction of information and meaning from audio signals for purposes such as word statistics, trend analysis, quality assurance, and the like. Audio analysis could be performed in audio interaction-extensive working environments, such as for example call centers, financial institutions, health organizations, public safety organizations or the like. Typically, audio analysis is used in order to extract useful information associated with or embedded within captured or recorded audio signals carrying interactions. Audio interactions contain valuable information that can provide enterprises with insights into their business, users, customers, activities and the like. The extracted information can be used for issuing alerts, generating reports, sending feedback or otherwise using the extracted information. The information can be usefully manipulated and processed, such as being stored, retrieved, synthesized, combined with additional sources of information, and the like. Extracted information can include for example, continuous speech, spotted words, identified speaker, extracted emotional (positive or negative) segments within an interaction, data related to the call flow such as number of bursts in from each side, segments of mutual silence, or the like. The customer side of an interaction recorded in a commercial organization can be used for various purposes such as trend analysis, competitor analysis, emotion detection (finding emotional calls) to improve customer satisfaction level, and the like. The service provider side of such interactions can be used for purposes such as script adherence, emotion detection (finding emotional calls) to track deficient agent behavior, and the like. The most common interaction recording format is summed audio, which is the product of analog line recording, observation mode and legacy systems. A summed interaction may include, in addition to two or more speakers that at times may talk simultaneously (co-speakers), also music, tones, background noises on either side of the interaction, or the like. The audio analysis performance, as measured in terms of accuracy, detection, real-time efficiency and resource efficiency, depends directly on the quality and integrity of the captured and/or recorded signals carrying the audio interaction, on the availability and integrity of additional meta-information, on the capabilities of the computer programs that constitute the audio analysis process and on the available computing resources. Many of the analysis tasks are highly sensitive to the audio quality of the processed interactions. Multiple speakers, as well as music (which is often present on hold periods), tones, background noises such as street noise, ambient noise, convolutional noises such as channel type and handset type, keystrokes and the like, severely degrade the performance of these engines, sometimes to the degree of complete uselessness, for example in the case of emotion detection where it is mandatory to analyze only one speaker&#39;s speech segments. Therefore it is crucial to identify only the speech segments of an interaction wherein a single speaker is speaking. The customary solution is to use unsupervised speaker segmentation module as part of the audio analysis. 
     Traditionally, unsupervised speaker segmentation algorithms are based on bootstrap (bottom up) classification methods, starting with short discriminative segments and extending such segments using additional, not necessarily adjacent segments. Initially, a homogenous speaker segment is located, and regarded as an anchor. The anchored segment is used for initially creating a model of the first speaker. In the next phase a second homogenous speaker segment is located, in which the speaker characteristics are most different from the first segment. The second segment is used for creating a model of the second speaker. By deploying an iterative maximum-likelihood (ML) classifier, based on the anchored speaker models, all other utterance segments could be roughly classified. The conventional methods suffer from a few limitations: the performance of the speaker segmentation algorithm is highly sensitive to the initial phase, i.e., poor choice of the initial segment (anchored segment) can lead to unreliable segmentation results. Additionally, the methods do not provide a verification mechanism for assessing the success of the segmentation, nor the convergence of the methods, in order to eliminate poorly segmented interactions from being further processed by audio analysis tools and providing further inaccurate results. Another drawback is that additional sources of information, such as computer-telephony-integration (CTI) data, screen events and the like are not used. Yet another drawback is the inability of the method to tell which collection of segments belongs to one speaking side, such as the customer, and which belongs to the other speaking side, since different analyses are performed on both sides, to serve different needs. 
     It should be easily perceived by one with ordinary skills in the art, that there is an obvious need for an unsupervised segmentation method and for an apparatus to segment an unconstrained interaction into segments that should not be analyzed, such as music, tones, low quality segments or the like, and segments carrying speech of a single speaker, where segments of the same speaker should be grouped or marked accordingly. Additionally, identifying the sides of the interaction is required. The segmentation tool has to be effective, i.e., extract as long and as many as possible segments of the interaction in which a single speaker is speaking, with as little as possible compromise on the reliability, i.e., the quality of the segments. Additionally, the tool should be fast and efficient, so as not to introduce delays to further processing, or place additional burden on the computing resources of the organization. It is also required that the tool will provide a performance estimation which can be used in deciding whether the speech segments are to be sent for analysis or not. 
     SUMMARY OF THE PRESENT INVENTION 
     It is an object of the present invention to provide a novel method for speaker segmentation which overcomes the disadvantages of the prior art. In accordance with the present invention, there is thus provided a speaker segmentation method for associating one or more segments for each of two or more sides of one or more audio interactions, with one of the sides of the interaction using additional information, the method comprising: a segmentation step for associating the one or more segments with one side of the interaction, and a scoring step for assigning a score to said segmentation. The additional information can be one or more of the group consisting of: computer-telephony-integration information related to the at least one interaction; spotted words within the at least one interaction; data related to the at least one interaction; data related to a speaker thereof; external data related to the at least one interaction; or data related to at least one other interaction performed by a speaker of the at least one interaction. The method can further comprise a model association step for scoring the segments against one or more statistical models of one side, and obtaining a model association score. The scoring step can use discriminative information for discriminating the two or more sides of the interaction. The scoring step can comprise a model association step for scoring the segments against a statistical model of one side, and obtaining a model association score. Within the method, the scoring step can further comprise a normalization step for normalizing the one or more model scores. The scoring step can also comprise evaluating the association of the one or more segments with a side of the interaction, using additional information. The additional information can be one or more of the group consisting of: computer-telephony-integration information related to the at least one interaction; spotted words within the at least one interaction; data related to the at least one interaction; data related to a speaker thereof; external data related to the at least one interaction; or data related to at least one other interaction performed by a speaker of the at least one interaction. The scoring step can comprise statistical scoring. The method can further comprise: a step of comparing the score to a threshold; and repeating the segmentation step and the scoring step if the score is below the threshold. The threshold can be predetermined, or dynamic, or depend on: information associated with said at least one interaction, information associated with an at least one speaker thereof, or external information associated with the interaction. The segmentation step can comprise a parameterization step to transform the speech signal to a set of feature vectors in order to generate data more suitable for statistical modeling; an anchoring step for locating an anchor segment for each side of the interaction; and a modeling and classification step for associating at least one segment with one side of the interaction. The anchoring step or the modeling and classification step can comprise using additional data, wherein the additional data is one or more of the group consisting of: computer-telephony-integration information related to the at least one interaction; spotted words within the at least one interaction; data related to the at least one interaction; data related to a speaker thereof; external data related to the at least one interaction; or data related to at least one other interaction performed by a speaker of the at least one interaction. The method can comprise a preprocessing step for enhancing the quality of the interaction, or a speech/non-speech segmentation step for eliminating non-speech segments from the interaction. The segmentation step can comprise scoring the one or more segments with a voice model of a known speaker. 
     Another aspect of the disclosed invention relates to a speaker segmentation apparatus for associating one or more segments for each of at two or more speakers participating in one or more audio interactions, with a side of the interaction, using additional information, the apparatus comprising: a segmentation component for associating one or more segments within the interaction with one side of the interaction; and a scoring component for assigning a score to said segmentation. Within the apparatus the additional information can be of the group consisting of: computer-telephony-integration information related to the at least one interaction; spotted words within the at least one interaction; data related to the at least one interaction; data related to a speaker thereof; external data related to the interaction; or data related to one or more other interactions performed by a speaker of the interaction. 
     Yet another aspect of the disclosed invention relates to a quality management apparatus for interaction-rich environments, the apparatus comprising: a capturing or logging component for capturing or logging one or more audio interactions; a segmentation component for segmenting the interactions; and a playback component for playing one or more parts of the one or more audio interactions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will be understood and appreciated more fully from the following detailed description taken in conjunction with the drawings in which: 
         FIG. 1  is a schematic block diagram of a typical environment in which the disclosed invention is used, in accordance with a preferred embodiment of the present invention; 
         FIG. 2  is a schematic flowchart of the disclosed segmentation method, in accordance with a preferred embodiment of the present invention; and 
         FIG. 3  is a schematic flowchart of the scoring process, in accordance with a preferred embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     The present invention overcomes the disadvantages of the prior art by providing a novel method and a system for locating segments within an audio interaction in which a single speaker is speaking, dividing the segments into two or more groups, wherein the speaker in each segment group is the same one, and discriminating in which group of segments a certain participant, or a certain type of participant, such as a service representative (agent) of an organization, is speaking, and in which group another participant or participant type, such as a customer, is speaking. The disclosed invention utilizes additional types of data collected in interaction-intensive environments, such as call centers, financial institutions or the like, in addition to captured or recorded audio interactions in order to enhance the segmentation and the association of a group of segments with a specific speaker or speaker type, such as an agent, a customer or the like. The discussion below is oriented more to applications involving commerce or service, but the method is applicable to any required domain, including public safety, financial organizations such as trade floors, health organizations and others. 
     The information includes raw information, such as meta data, as well as information extracted by processing the interactions. Raw information includes, for example Computer Telephony Integration (CTI) information which includes hold periods, number called, number called, DNIS, VDN, ANI or the like, agent details, screen events related to the current or other interactions with the customer, information exchanged between the parties, and other relevant information that can be retrieved form external sources such as CRM data, billing information, workflow management, mail messages and the like. The extracted information can include, for example certain words spotted within the interaction, such as greetings, compliance phrases or the like, continuous speech recognition, emotion detected within an interaction, and call flow information, such as bursts of one speaker when the other speaker is talking, mutual silence periods and others. Other data used, include for example voice models of a single or multiple speakers. 
     The collected data is used in the process of segmenting the audio interaction in a number of ways. First, the information can be used to obtain an accurate anchor point for the initial selection of a segment of a single speaker. For example, a segment in which a compliance phrase was spotted can be a good anchor point for one speaker, specifically the agent. A highly emotional segment can be used as an anchor for the customer side. Such information can be used during the classification of segments into speakers, and also for posteriori assessment of the performance of the segmentation. Second, the absence or presence, and certainty level of specific events within the segments of a certain speaker can contribute to the discrimination of the agent side from the customer side, and also for assessing the performance of the segmentation. For example, the presence of compliance sentences and typical customer-side noises (such as a barking dog) in segments of allegedly the same speaker, can suggest a deficient segmentation. The discrimination of the speakers can be enhanced by utilizing agent-customer-discriminating information, such as screen events, emotion levels, and voice models of a specific agent, a specific customer, a group of agents, a universal agent model or a universal customer model. If segments attributed to one side have a high probability of complying with a specific agent&#39;s characteristics or with a universal agent model, relating the segments to the agent side will have a higher score, and vice versa. Thus, the segmentation can be assessed, and according to the assessment result accepted, rejected, or repeated. 
     Referring now to  FIG. 1 , which presents a block diagram of the main components in a typical environment in which the disclosed invention is used. The environment, generally referenced as  10 , is an interaction-rich organization, typically a call center, a bank, a trading floor, another financial institute, a public safety contact center, or the like. Customers, users or other contacts are contacting the center, thus generating input information of various types. The information types include vocal interactions, non-vocal interactions and additional data. The capturing of voice interactions can employ many forms and technologies, including trunk side, extension side, summed audio, separate audio, various encoding and decoding protocols such as G729, G726, G723.1, and the like. The vocal interactions usually include telephone  12 , which is currently the main channel for communicating with users in many organizations. The voice typically passes through a PABX (not shown), which in addition to the voice of the two or more sides participating in the interaction collects additional information discussed below. A typical environment can further comprise voice over IP channels  16 , which possibly pass through a voice over IP server (not shown). The interactions can further include face-to-face interactions, such as those recorded in a walk-in-center  20 , and additional sources of vocal data  24 , such as microphone, intercom, the audio of video capturing, vocal input by external systems or any other source. In addition, the environment comprises additional non-vocal data of various types  28 . For example, Computer Telephony Integration (CTI) used in capturing the telephone calls, can track and provide data such as number and length of hold periods, transfer events, number called, number called from, DNIS, VDN, ANI, or the like. Additional data can arrive from external sources such as billing, CRM, or screen events, including text entered by a call representative, documents and the like. The data can include links to additional interactions in which one of the speakers in the current interaction participated. Another type of data includes data extracted from vocal interactions, such as spotted words, emotion level, speech-to-text or the like. Data from all the above-mentioned sources and others is captured and preferably logged by capturing/logging unit  32 . The captured data is stored in storage  34 , comprising one or more magnetic tape, a magnetic disc, an optical disc, a laser disc, a mass-storage device, or the like. The storage can be common or separate for different types of captured interactions and different types of additional data. Alternatively, the storage can be remote from the site of capturing and can serve one or more sites of a multi-site organization such as a bank. Capturing/logging unit  32  comprises a computing platform running one or more computer applications as is detailed below. From capturing/logging unit  32 , the vocal data and preferably the additional relevant data is transferred to segmentation component  36  which executes the actual segmentation of the audio interaction. Segmentation component  36  transfers the output segmentation to scoring component  38 , which assigns a score to the segmentation. If the score exceeds a certain threshold, the segmentation is accepted. If the score is below the threshold, another activation of the segmentation is attempted. The scoring and segmentation sequence is repeated until an acceptable score is achieved, or a stopping criterion is met. The threshold can be predetermined, or it can be set dynamically, taking into account the interaction type, one or more of the speakers if known, additional data such as Computer-Telephony-Integration (CTI) data, CRM, or billing data, data associated with any of the speakers, screen events or the like. For example, the system can assign a higher threshold to an interaction of a VIP customer, than to an interaction of an ordinary customer, or higher threshold for interactions involving opening an account or the like. It is obvious that if the audio content of interactions, or some of the interactions, is recorded as summed, then speaker segmentation has to be performed. However, even when the audio interactions are recorded separately for each side, as is usually the case in trunk-side or digital extension recording, there still is segmentation work to be done. Separating speech from non-speech is required in order to obtain fluent speech segments, by excluding segment of music, tones, significant background noise, low quality or the like. In addition, there might still be effects of echo, background speech on the either side, the customer consulting a third person, or the like, which require the segmentation and association of single-speaker segments with one speaker. The segmented audio can assume the form of separate audio streams or files for each side, the form of the original stream or file accompanied by indexing information denoting the beginning and end of each segment in which a certain side of the interaction is speaking, or any other form. The segmented audio is preferably transferred to further engines  40 , such as speech-to-ext engine, emotion detection, speaker recognition, or other voice processing engines. Alternatively, the segmentation information or the segmented voice is transferred for storage purposes  44 . In addition, the information can be transferred to any other purpose or component  48 , such as, but not limited to a playback component for playing the captured or segmented audio interactions. All components of the system, including capturing/logging components  32  and segmentation component  36 , preferably comprise one or more computing platforms, such as a personal computer, a mainframe computer, or any other type of computing platform that is provisioned with a memory device (not shown), a CPU or microprocessor device, and several I/O ports (not shown). Alternatively, each component can be a DSP chip, an ASIC device storing the commands and data necessary to execute the methods of the present invention, or the like. Each component can further include a storage device (not shown), storing the relevant applications and data required for processing. Each application running on each computing platform, such as the capturing applications or the segmentation application is a set of logically inter-related computer programs or modules and associated data structures that interact to perform one or more specific tasks. All applications can be co-located and run on the same one or more computing platform, or on different platforms. In yet another alternative, the information sources and capturing platforms can be located on each site of a multi-site organization, and one or more segmentation components can be remotely located, segment interactions captured at one or more sites and store the segmentation results in a local, central, distributed or any other storage. 
     Referring now to  FIG. 2  showing a flowchart of the main steps in the proposed speaker segmentation method. Summed audio as well as additional data, such as CTI data, screen events, spotted words, data from external sources such as CRM, billing, or the like are introduced at step  104  to the system. The summed audio can use any format and any compression method acceptable by the system, such as PCM, WAV, MP3, G729, G726, G723.1, or the like. The audio can be introduced in streams, files, or the like. At step  108 , preprocessing is performed on the audio, in order to enhance the audio for further processing. The preprocessing preferably includes decompression, according to the compression used in the specific interaction. If the audio is from an external source, the preprocessing can include compression and decompression with one of the protocols used in the environment in order to adapt the audio to the characteristics common in the environment. The preprocessing can further include low-quality segments removal or other processing that will enhance the quality of the audio. Step  110  marks, removes or otherwise eliminates non-speech segments from the audio. Such segments can include music, tones, DMFT, silence, segments with significant background noise or other substantially non-speech segments. Preprocessing step  108  and speech/non-speech segmentation step  110  are optional, and can be dispensed with. However, the performance in time, computing resources and the quality of the speaker segmentation will degrade if step  108  or step  110  are omitted. The enhanced audio is then transferred to segmentation step  112 . Segmentation step  112  comprises a parameterization step  118 , an anchoring step  120  and a modeling and classification step  124 . At step  118  the speech is being parameterized by transforming the speech signal into a set of feature vectors. The purpose of this transformation is to obtain a new representation which is more compact, less redundant and more suitable for statistical modeling. Most of the speaker segmentation systems depend on cepstral representation of speech in addition to prosodic parameters such as pitch, pitch variance, energy level and the like. The parameterization generates a sequence of feature vectors, wherein each vector relates to a certain time frame, preferably in the range of 10-30 ms, where the speech could be regarded as stationary. In another alternative method, the parameterization step is performed earlier as part of preprocessing step  108  or speech/non-speech segmentation step  110 . At step  118  the speech signal is being divided into non-overlapping segments, typically but not limited to having a period of 1-3 seconds. The speaker segmentation main process starts at step  120 , during which, anchor segments are located within the audio interaction. Preferably, the method searches for two segments to be used as anchor segments and each of the two segments should contain speech of a different speaker. Each anchor segment will be used for initial voice modeling of the speaker it represents. The first anchor segment finding is preferably performed by a statistical modeling of every segment in the interaction and then by locating the most homogenous segment in terms of statistical voice feature distribution. Such segment is more likely to be a segment in which a single speaker is speaking rather than an area of transition between two speakers. This segment will be used for first speaker initial voice model building. Locating such first segment can also involve utilizing additional data, such as CTI events, for example the first speaker in a call center interaction is likely to be the agent addressing the customer. Alternatively, spotting with high certainty standard phrases which agents are instructed to use, such as “company X good morning, how can I help you”, can help identify an anchor segment for the agent side, and standard questions, such as “how much would it cost to”, can help in locating homogenous segments of a customer side. Once the first anchor segment is determined, the method constructs a statistical model of the voice features in that segment where the statistical model represents the voice characteristics of the first speaker. Subsequently, the method searches for a second anchor segment, whose statistical model is as different as possible from the statistical model of the first anchor, the distance is measured and quantified by some statistical distance function, such as a likelihood ratio test. The aim of the second anchor finding is to find an area in the interaction which is most likely produced by a different statistical source, i.e. a different speaker. Alternatively, if the agent (or the customer) is known and a voice model of the agent has previously been built using other voice samples of the speaker or can be otherwise obtained, locating the segments of the agent can be done by searching for all segments which comply with the specific agent model, and continuing by associating all the rest of the speech segment with the customer (or agent) side. Once the two anchor segments are determined, the system goes into the modeling and classification step  124 . Step  124  comprises an iterative process. On each iteration, a statistical model is constructed from the aggregated segments identified so far as belonging to each speaker. Then the distance between each segment in the interaction and the speakers voice models is measured and quantified. The distance can be produced by likelihood calculation or the like. Next, one or more segments which are most likely to come from the same statistical distributions as the speakers statistical models, i.e. produced by the same speaker, are added to the similar speaker&#39;s pool of segments from the previous iteration. On the next iteration, the statistical models are reconstructed, utilizing the newly added segments as well as the previous ones, and new segments to be added are searched for. The iterations proceed until one or more stopping criteria are met, such as the distance between the model and the most similar segment exceeding a certain threshold, the length of the added segments being below a certain threshold or the like. During modeling and classification step  124 , soft classification techniques can also be applied in determining the similarity between a segment and a statistical model or when calculating if a stop criterion is met. Once the modeling and classification is done, scoring step  128  takes place. Scoring step  128  assigns a score to the segmentation result. If the score is below a predetermined threshold, the performance is unsatisfactory and the process repeats, restarting from step  120 , excluding the former first and second anchor segments or from step  118  using different voice features. The threshold can be predetermined, or it can be set dynamically, taking into account the interaction type, other data related to the interaction, additional data such as CTI data, external data such as CRM or billing data, data associated with any of the speakers, screen events or the like. The stopping condition for the segmentation can be defined in a predetermined manner, such as “try at most X times, and if the segmentation does not succeed, skip the interaction and segment another one”. Alternatively, the stopping criteria can be defined dynamically, for example, “continue the segmentation as long as there are still segments that no segments X or less seconds apart from them, have been used as anchor segments”. If the segmentation score exceeds the predetermined threshold, the results are output at step  144 . The Scoring process is detailed in association with  FIG. 3  below. The results output at step  144  can take any required form. One preferred form is a file or stream containing text, denoting the start and end locations of each segment, for example in terms of time units from the beginning of the interaction, and the associated speaker. The output can also comprise start and end locations for segments of an unknown speaker, or for non-speech segments. Another preferred form comprises two or more files wherein each file comprises the segments of one speaker. The non-speech or unknown speaker segments can be ignored or reside in a separate file for purposes such as playback. 
     Referring now to  FIG. 3  showing the main steps in the scoring assessment process referred to in step  140  of  FIG. 2 . The scoring step comprises two main parts, assessing a statistical score and an agent-customer discrimination score. The statistical score determined at step  204  is based on determining the distance between the model generated from the segments attributed to one side and the model generated from the segments attributed to the other side. If the distance between the models is above a predetermined threshold, then the segments attributed to one side are significantly different than the segments attributed to the other side, and the classification is considered successful. If the distance is below a predetermined threshold (not necessarily equal to the predetermined threshold mentioned above), the segments attributed to different speakers are not distinctive enough, and the classification is assumed to be unsuccessful. However, the statistical score can be problematic, since the model-distance determination is calculated using the same tools and principles used when assigning segments to a certain speaker during the classification step. Therefore, the segmentation step and the testing step use the same data and the same calculations, which makes the examination biased and less reliable. Discriminative scoring step  208  uses discriminative information, such as discriminative customer-agent information in order to assess the success of the speaker segmentation process, and to determine or verify the association of each segment group with a specific speaker. Discriminative scoring step  208  is divided into model association step  212  and additional information scoring step  216 . Model association step  212  uses previously built or otherwise acquired universal models of agents and of customers. The universal agent model is built from speech segments in which multiple agents of the relevant environment are speaking, using the same types of equipment used in the environment. The universal customer model is built from multiple segments of customers using various types of equipment, including land lines, cellular lines, various handsets, various types of typical customer background noise and the like. The model preferably incorporates both male and female customers if customers of both genders are likely to speak in real interactions, customers of relevant ages, accents and the like. If the speaker segmentation includes side (agent/customer) association, step  212  is used for verification of the association; otherwise step  212  is used for associating each segment group with a specific side. In model association step  212 , the speech segments attributed to each side are preferably scored against the universal agent model in step  220 , and against the universal customer model in step  224 , thus obtaining two model association scores. The two model association scores are normalized in normalization step  228 . If one segment group was assigned, for example, to an agent, and indeed the normalized score against the universal agent model yielded a significantly higher score than the scoring against the universal customer model, the association of the segment group to the agent side is reinforced. However, if the score of agent-assumed segment group against a customer model is higher then the score against the genera agent model, this might indicate a problem either in the segmentation or in the side association. The scoring can be performed for the segments attributed to a certain side one or more at a time, or all of them together, using a combination of the feature vectors associated with the segments. If the segment group is not assigned to a specific side, a normalized score to one side which exceeds a certain threshold can be used in determining the side as well as the quality of the segmentation. Model association step  212  can be performed solely in order to associate a segment group with a certain side, and not just to assess a segmentation quality, in which case it is not part of discriminative score  208  but rather an independent step. 
     In step  232  the method further uses additional data evaluation, in order to evaluate the contribution of each segment attributed to a certain speaker. Additional data can include spotted words that are typical to a certain side, such as “how can I help you” on the agent side, and “how much would that cost” for a customer side, CTI events, screen events, external or internal information or the like. The presence, possibly associated with a certainty level, of such events on segments associated with a specific side are accumulated or otherwise combined into a single additional data score. The scores of statistical scoring  204 , model association  212  and additional data scoring  232  are combined at step  236 , and a general score is issued. If the score is below a predetermined threshold, as is evaluate at step  140  of  FIG. 2 , the segmentation process restarts at step  120  excluding the former first and second anchor segments. Since none of scoring steps  204 ,  212 , and  232  is mandatory, combining step  236  weights whatever scores that are available. Each subset of the scoring results of scoring steps  204 ,  212  and  232  can be used to produce a general scoring result. Combining step  236  can be further designed to weight additional scores, such as user input or other scoring mechanisms currently known or that will become known at a later time. Combining step  236  can use dynamic or predetermined parameters and schemes to weight or otherwise combine the available scores. 
     As mentioned above in relation to the statistical model scoring, and is applicable for all types of data, the same data item should not be used in the scoring phase if it was already used during the segmentation phase. Using the same data item in the two phases will bias the results and give higher and unjustified score to certain segmentation. For example, if the phrase “Company X good morning” was spotted at a certain location, and the segment it appeared on was used as an anchor for the agent side, considering this word during additional data scoring step will raise the score in an artificial manner, since it is known that the segment the phrase was said in is associated with the agent side. 
     It will be appreciated by people skilled in the art that some of the presented methods and scorings can be partitioned in a different manner over the described steps without significant change in the results. It will also be appreciated by people skilled in the art that additional scoring methods can exist and be applied in addition, or instead of the presented scoring. The scoring method can be applied to the results of any segmentation method, and not necessarily the one presented above. Also, different variations can be applied to the segmentation and the scoring methods as described, without significant change to the proposed solution. It will further be appreciated by people skilled in the art that the disclosed invention can be extended to segmenting an interaction between more than two speakers, without significant changes to the described method. The described rules and parameters, such as the acceptable score values, stopping criteria for the segmentation and the like can be predetermined or set dynamically. For example, the parameters can tale into account the type or length of the interaction, the customer type as received from an external system or the like. 
     The disclosed invention provides a novel approach to segmenting an audio interaction into segments, and associating each group of segments with one speaker. The disclosed invention provides a scoring and control mechanism over the quality of the resulting segmentation. The system 
     It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. Rather the scope of the present invention is defined only by the claims which follow.