Abstract:
Systems and methods for processing audio are provided. The system may include a processor to convert an audio input received via a call to text. The processor may perform a comparison between a portion of the text to one or more phrases included in a table. The processor may also make a selection of at least one of a first object or a first action based on the comparison. The processor may further route the call based on the at least one of the first object or the first action

Description:
CLAIM OF PRIORITY 
       [0001]    This application is continuation of, and claims priority from, U.S. patent application Ser. No. 13/644,305, filed on Oct. 4, 2012, and entitled “SYSTEM AND METHOD FOR PROCESSING SPEECH,” now allowed, which is a continuation of U.S. patent application Ser. No. 12/750,792, filed on Mar. 31, 2010 and now U.S. Pat. No. 8,306,192, issued on Nov. 6, 2012, which is a continuation of U.S. patent application Ser. No. 11/809,817, filed Jun. 1, 2007, now U.S. Pat. No. 7,720,203, issued May 18, 2010, which is a continuation of U.S. patent application Ser. No. 11/005,494, filed on Dec. 6, 2004 and now U.S. Pat. No. 7,242,751, issued Jul. 10, 2007, each of which is hereby incorporated by reference in its entirety. 
     
    
     FIELD OF THE DISCLOSURE 
       [0002]    The present disclosure relates generally to speech recognition and, more particularly, to speech recognition-enabled automatic call routing service systems and methods. 
       BACKGROUND 
       [0003]    Speech recognition systems are specialized computers that are configured to process and recognize human speech and may also take action or carry out further processes. Developments in speech recognition technologies support “natural language” type interactions between automated systems and users. A natural language interaction allows a person to speak naturally. Voice recognition systems can react responsively to a spoken request. An application of natural language processing is speech recognition with automatic call routing (ACR). A goal of an ACR application is to determine why a customer is calling a service center and to route the customer to an appropriate agent or destination for servicing a customer request. Speech recognition technology generally allows an ACR application to recognize natural language statements so that the caller does not have to rely on a menu system. Natural language systems allow the customer to state the purpose of their call “in their own words.” 
         [0004]    In order for an ACR application to properly route calls, the ACR system attempts to interpret the intent of the customer and selects a routing destination. When a speech recognition system partially understands or misunderstands the caller&#39;s intent, significant problems can result. Further, even in touch-tone ACR systems, the caller can depress the wrong button and have a call routed to a wrong location. When a caller is routed to an undesired system and realizes that there is a mistake, the caller often hangs up and retries the call. Another common problem occurs when a caller gets “caught” or “trapped” in a menu that does not provide an acceptable selection to exit the menu. Trapping a caller or routing the caller to an undesired location leads to abandoned calls. Most call routing systems handle a huge volume of calls and, even if a small percentage of calls are abandoned, the costs associated with abandoned calls are significant. 
         [0005]    Current speech recognition systems, such as those sold by Speechworks™, operate utilizing a dynamic semantic model. The semantic model recognizes human speech and creates multiple word strings based on phonemes that the semantic model can recognize. The semantic model assigns probabilities to each of the word strings using rules and other criteria. However, the semantic model has extensive tables and business rules, many that are “learned” by the speech recognition system. The learning portion of the system is difficult to set up and modify. Further, changing the word string tables in the semantic model can be an inefficient process. For example, when a call center moves or is assigned a different area code, the semantic system is retrained using an iterative process. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0006]      FIG. 1  illustrates a simplified configuration of a telecommunication system; 
           [0007]      FIG. 2  is a general diagram that illustrates a method of routing calls; 
           [0008]      FIG. 3  is a flow diagram that illustrates a method of processing and routing calls; 
           [0009]      FIG. 4  is a table that depicts speech input and mapped synonym terms; and 
           [0010]      FIG. 5  is a table illustrating action-object pairs and call destinations relating to the action object pairs. 
       
    
    
     DETAILED DESCRIPTION 
       [0011]    In a particular embodiment, a speech recognition system includes a speech recognition interface and a processor coupled to the speech recognition interface. The processor converts speech received from a call at the speech recognition interface to at least one word string. The processor parses each word string of the at least one word string into first objects and first actions. The processor accesses a synonym table to determine second objects and second actions based on the first objects and the first actions. The processor also selects a preferred object and a preferred action from the second objects and the second actions. 
         [0012]    In a particular embodiment, a computerized method of processing speech includes determining a plurality of objects based on speech input and determining a plurality of actions based on the speech input. The computerized method includes comparing the objects and the actions with entries in a synonym table to determine synonym objects and synonym actions. The computerized method includes selecting a preferred object and a preferred action from the synonym objects and the synonym actions. The computerized method also includes routing a call that provided the speech input to a destination based on the preferred object and the preferred action. 
         [0013]    In a particular embodiment, a computerized method includes transforming speech input from a caller into a plurality of word strings. The computerized method includes converting the word strings into pairs of objects and actions. The computerized method includes determining from a synonym table synonym pairs from the pairs. The computerized method also includes selecting a preferred pair from the synonym pairs. 
         [0014]    Particular systems and particular methods are disclosed for processing a call by receiving caller input in a speech format and utilizing phonemes to convert the speech input into word strings. The word strings are then converted into at least one object and at least one action. A synonym table is utilized to determine actions and objects. Objects generally represent nouns and adjective-noun combinations while actions generally represent verbs and adverb-verb combinations. The synonym table stores natural language phrases and their relationship with actions and objects. The actions and objects are utilized to determine a routing destination utilizing a routing table. The call is then routed based on the routing table. During the process, the word string, the actions, the objects and an action-object pair can be assigned a probability value. The probability value represents a probability that the word string, the action, or the object accurately represent the purpose or intent of the caller. 
         [0015]    Referring to  FIG. 1 , an illustrated communications system  100  that includes a call routing support system is shown. The communications system  100  includes a speech enabled call routing system (SECRS)  118 , such as an interactive voice response system having a speech recognition module. The system  100  includes a plurality of potential call destinations. Illustrative call destinations shown include service departments, such as billing department  120 , balance information  122 , technical support  124 , employee directory  126 , and new customer service departments  128 . The communication network  116  receives calls from a variety of callers, such as the illustrated callers  110 ,  112 , and  114 . In a particular embodiment, the communication network  116  may be a public telephone network or may be provided by a voice over Internet protocol (VoIP) type network. The SECRS  118  may include components, such as a processor  142 , a synonym table  144 , and an action-object routing module  140 . The SECRS  118  is coupled to and may route calls to any of the destinations, as shown. In addition, the SECRS  118  may route calls to an agent, such as the illustrated live operator  130 . An illustrative embodiment of the SECRS  118  may be a call center having a plurality of agent terminals attached (not shown). Thus, while only a single operator  130  is shown, it should be understood that a plurality of different agent terminals or types of terminals may be coupled to the SECRS  118 , such that a variety of agents may service incoming calls. In addition, the SECRS  118  may be an automated call routing system. In a particular embodiment, the action-object routing module  140  includes an action-object lookup table for matching action-object pairs to desired call routing destinations. 
         [0016]    Referring to  FIG. 2 , an illustrative embodiment of an action-object routing module  140  is shown. In this particular embodiment, the action-object routing module  140  includes an acoustic processing model  210 , semantic processing model  220 , and action-object routing table  230 . The acoustic model  210  receives speech input  202  and provides text  204  as its output. Semantic model  220  receives text  204  from the acoustic model  210  and produces an action-object pair  206  that is provided to the action-object routing table  230 . The routing table  230  receives action-object pairs  206  from semantic model  220  and produces a desired call routing destination  208 . Based on the call routing destination  208 , a call received at a call routing network  118  may be routed to a final destination, such as the billing department  120  or the technical support service destination  124  depicted in  FIG. 1 . Ina particular embodiment, the action-object routing table  230  may be a look up table or a spreadsheet, such as Microsoft Excel™. 
         [0017]    Referring to  FIG. 3 , an illustrative embodiment of a method of processing a call using an automated call routing system is illustrated. The method starts at  300  and proceeds to step  302  where a speech input signal, such as a received utterance, is received or detected. Using phonemes, the received speech input is converted into a plurality of word strings or text in accordance with an acoustic model, as shown at steps  304  and  306 . In a particular embodiment, probability values are assigned to word strings based on established rules and the coherency of the word string. Next, at step  308 , the word strings are parsed into objects and actions. Objects generally represent nouns and adjective-noun combinations while actions generally represent verbs and adverb-verb combinations. The actions and objects are assigned confidence values or probability values based on how likely they are to reflect the intent of the caller. In a particular embodiment a probability value or confidence level for the detected action and the detected object is determined utilizing the probability value of the word string used to create the selected action and the selected object. 
         [0018]    Many possible actions and objects may be detected or created from the word strings. The method attempts to determine and select a most probable action and object from a list of preferred objects and actions. To aid in this resolution a synonym table, such as the synonym table of  FIG. 4  can be utilized to convert detected actions and objects into preferred actions and objects. Thus, detected objects and actions are converted to preferred actions and objects and assigned a confidence level. The process of utilizing the synonym table can alter the confidence level. The synonym table stores natural language phrases and their relationship with a set of actions and objects. Natural language spoken by the caller can be compared to the natural language phrases in the table. Using the synonym table, the system and method maps portions of the natural phrases to detected objects and maps portions of the natural spoken phrase to detected actions. Thus, the word strings are converted into objects and actions, at steps  310  and  312  respectively and the selected action and object are set to the action and object that will be utilized to route the call. The action and object with the highest confidence value are selected based on many criteria such as confidence value, business rules, etc., in steps  310  and  312 . 
         [0019]    At step  310  and  312 , multiple actions and objects can be detected and provided with a probability value according to the likelihood that a particular action or object identifies a customer&#39;s intent and thus will lead to a successful routing of the call and a dominant action and dominant object are determined. Next, at step  314 , dominant objects and actions are paired together. At step  316 , a paired action-object is compared to an action-object routing table, such as the action object routing table of  FIG. 5 . The action-object routing table in  FIG. 5  is generally a predetermined list. When objects and actions find a match, then the destination of the call can be selected at step  318 , and the call is routed, at step  320 . The process ends at step  322 . 
         [0020]    Referring to  FIG. 4 , as an example, it is beneficial to convert word strings such as “I want to have” to actions such as “get.” This substantially reduces the size of the routing table. When a call destination has a phone number change, a single entry in the routing table may accommodate the change. Prior systems may require locating numerous entries in a voluminous database, or retraining a sophisticated system. In accordance with the present system, dozens of differently expressed or “differently spoken” inputs that have the same caller intent can be converted to a single detected action-object pair. Further, improper and informal sentences as well as slang can be connected to an action-object pair that may not bear phonetic resemblance to the words uttered by the caller. With a directly mapped lookup table such as the table in  FIG. 4 , speech training and learning behaviors found in conventional call routing systems are not required. The lookup table may be updated easily, leading to a low cost of system maintenance. 
         [0021]    In addition, the method may include using a set of rules to convert a word string into an object or action. In a particular example, geographic designation information, such as an area code, may be used to distinguish between two potential selections or to modify the probability value. In the event that the lookup table of the action-object pair does not provide a suitable response, such as where no entry is found in the routing table, the call may be routed to a human operator or agent terminal in response to a failed access to the action-object lookup table. 
         [0022]    Traditional automatic call routing systems are able to assign a correct destination 50-80% of the time. Particular embodiments of the disclosed system and method using action-object tables can assign a correct destination 85-95% of the time. Due to higher effective call placement rates, the number of abandoned calls (i.e., caller hang-ups prior to completing their task) is significantly reduced, thereby reducing operating costs and enhancing customer satisfaction. In addition, the automated call-routing system offers a speech recognition interface that is preferred by many customers to touch tone systems. 
         [0023]    The disclosed system and method offers significant improvements through decreased reliance on the conventional iterative semantic model training process. With the disclosed system, a semantic model assigns an action-object pair leading to increased call routing accuracy and reduced costs. In particular implementations, the correct call destination routing rate may reach the theoretical limit of 100%, depending upon particular circumstances. In some cases, certain action-object systems have been implemented that achieve a 100% coverage rate, hit rate, and call destination accuracy rate. 
         [0024]    The disclosed system and method is directed generally to integration of action-object technology with speech enabled automated call routing technology. The integration of these two technologies produces a beneficial combination as illustrated. The illustrated system has been described in connection with a call center environment, but it should be understood that the disclosed system and method is applicable to other user interface modalities, such as web-based interfaces, touchtone interfaces, and other speech recognition type systems. The disclosed system and method provides for enhanced customer satisfaction because the customer&#39;s intent can be recognized by an action-object pair and a high percentage of calls reach the intended destination. 
         [0025]    The above-disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments that fall within the scope of the present invention. Thus, to the maximum extent allowed by law, the scope of the present invention is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description.