Abstract:
A method for customized voice communication comprising receiving a speech signal, retrieving a user account including an user profile corresponding to an identifier of a caller producing the speech signal, and determining if the user profile includes a speech profile with at least one dialect. If the user profile includes a speech profile, the method further comprises analyzing using a speech analyzer on the speech signal to classify the speech signal into a classified dialect, comparing the classified dialect with each of the dialects in the user profiles to select one of the dialects, and using the selected dialect for subsequent voice communication with the user. The selected dialect can be used for subsequent recognition and response speech synthesis. Moreover, a method is described for storing a user&#39;s own pronunciation of names and addresses, whereby a user may be greeted by the communication device using their own specific pronunciation.

Description:
FIELD OF THE INVENTION 
       [0001]    This invention relates to a system, method and program for customizing voice recognition and voice synthesis for a specific user. In particular, this invention relates to adapting voice communication to account for the manner, style and dialect of a user. 
       BACKGROUND 
       [0002]    Many systems use voice recognition and voice synthesis for communicating between a machine and a person. These systems generally use a preset dialect and style for the interaction. The preset dialect is used for voice recognition and synthesis. For example, a call center uses one preset dialect for a given country. Additionally, the dialogs most commonly used are limited, such as “Press 1 for English, Press 2 for Spanish” etc. These systems only focus on what people say, rather than how the person is saying it. 
         [0003]    Furthermore, when addressing a person or confirming a name and address, the most common pronunciation of the name is used, even if the pronunciation varies on an individual basis. Alternatively, the user must spell the first few letters of the name for the system to recognize the name. 
       SUMMARY OF THE INVENTION 
       [0004]    Accordingly, disclosed is a method for customized voice communication comprising receiving a speech signal, retrieving an user account including an user profile corresponding to an identifier of a caller producing the speech signal, and determining if the user profile include a speech profile including at least one dialect. If the user profile includes a speech profile, the method further comprises analyzing using a speech analyzer the speech signal to classify the speech signal into a classified dialect, comparing the classified dialect with each of the at least one dialect in the user profile to select one of the at least one dialect; and using the selected one of the at least one dialect for subsequent voice communication based upon the comparing including subsequent recognition and response speech synthesis. 
         [0005]    Also disclosed is a method for customized voice communication comprising receiving a speech signal, retrieving an user account including an user profile corresponding an identifier of a caller producing the speech signal, obtaining a textual spelling of a word in the user profile; searching a pronunciation dictionary for a list of available pronunciations for the word; analyzing using a speech analyzer the speech signal to obtain a user pronunciation for the word to output a processed result, comparing the processed result with each of the available pronunciations in the list of available pronunciation, selecting a pronunciation for the word based upon the comparing, and using the selected pronunciation for subsequent voice communication. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0006]    The invention is further described in the detailed description that follows, by reference to the noted drawings by way of non-limiting illustrative embodiments of the invention, in which like reference numerals represent similar parts throughout the drawings. As should be understood, however, the invention is not limited to the precise arrangements and instrumentalities shown. In the drawings: 
           [0007]      FIG. 1  illustrates an exemplary voice communication system in accordance with the invention; 
           [0008]      FIG. 2  illustrates a flow chart for customizing a pronunciation of a name on an individual basis in accordance with the invention; 
           [0009]      FIG. 3  illustrates a second exemplary voice communication system in accordance with the invention; 
           [0010]      FIG. 4  illustrates a flow chart for a customized voice communication on an individual basis in accordance with the invention; 
           [0011]      FIG. 5  illustrates a flow chart for voice analysis in accordance with the invention; and 
           [0012]      FIG. 6  illustrates a flow chart for updating a dialect in accordance with the invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0013]    Inventive systems, methods and programs for customizing voice communication are presented. The systems, methods and programs described herein allow for individually tailored voice communication between an individual and a machine, such as a computer. 
         [0014]      FIG. 1  illustrates an exemplary voice communication system  1  according to the invention. The voice communication system  1  can be a system used in a call center, by providers of IVR (Interactive Voice Response) systems, service integrators, health care providers, drug companies, security companies, providers of speech security solutions, hotels and providers of hotel systems, sales staff; brokerage firms, on-line computer video games, schools, and universities. The use of the voice communication system  1  is not limited to the listed locations and can be used in any automated inbound and outbound user contact. The voice communication system  1  allows a voice to be synthesized to greet a person by name, using their own pronunciation for their name, street address or any other word or phrase. 
         [0015]    The voice communication system  1  includes a communications device  10 , a phonetic speech analyzer  20 , a processor  40 , and a text-to-speech converter  45 . Additionally, the voice communication system  1  includes user profile storage  25 , a name dictionary  30  and pronunciation rules storage  35 . 
         [0016]    The communications device  10  can be any device capable of communication. For example, the communications device  10  can be, but is not limited to, a cellular telephone, PDA, wired telephone, a network enabled video game console or a computer. The communications device  10  can communicate using any available network, such as, public switched telephone network (PSTN), cellular (RF networks), other wireless telephone or data network, fiber optics and the Internet or the like.  FIG. 1  illustrates the communications device  10  separate from the processor  40 , however, the two can be integrated. 
         [0017]    The processor  40  can be a CPU having volatile and non-volatile memory. The processor  40  is programmed with a program that causes the processor  40  to execute the methods described herein. Alternatively, the processor  40  can be an application-specific integrated circuit (ASIC), a digital signal processing chip (DSP), field programmable gate array (FPGA), programmable logic array (PLA) or the like. 
         [0018]    The phonetic speech analyzer  20  also can be included in the processor  40 . For illustrative purposes,  FIG. 1  illustrates the phonetic speed analyze  20  separately. The phonetic speech analyzer  20  can be software based, for example, being built into a software application run on the processor  40 . Additionally, the phonetic speech analyzer  20  can be partially or totally built into the hardware. A partial hardware implementation can be, for example, the implementation of functions in integrated circuits and having the functions invoked by a software application. The phonetic speech analyzer  20  analyzes the speech pattern and outputs a likely set of phonetic classes for each of the sampling periods. For example, the classes can be a) fricative, liquid glide, front (mid-open vowel), voiced dental, unvoiced velar, back (closed vowel), etc or b) Hidden Markov Models (“HMM”) of Cepstral coefficients, or (c) any other method for speech recognition. The classes are stored in the processor  40 . 
         [0019]    The user profile storage  25  is a database of all user accounts that have registered with a particular organization or entity that is using the voice communication system  1 . The user profile includes identifying information, such as a user name, a telephone number, and address. The user profile can be indexed by telephone number or any equivalent unique identifier. Additionally, the user profile can include any special pronunciation for the name and/or address previously determined. 
         [0020]    The name dictionary  30  contains a list by name of common (and not so common) pronunciations of names for people and places. The name dictionary  30  can include a ranking system that ranks the pronunciations by likely pronunciations, i.e., more common pronunciations are listed first. Additionally, if the pronunciations are ranked, the ranking can include different tiers. The first tier includes the most common pronunciation group, the second tier includes the second most common pronunciation group and so on. Initially, when the name dictionary  30  is checked for pronunciations, the pronunciations in the first tier are provided. Sequential pronunciation retrievals for the same name provide additionally tiers for comparisons. 
         [0021]    The pronunciation rules storage  35  includes common rules for pronunciation (the “Rules”). The Rules  35  can be used when a match was not found via the name dictionary  30  and speech analysis. Additionally, the Rules  35  can be used to confirm the findings of the name dictionary  30  and speech analysis. The Rules  35  are letter-to-sound rules, such as provided by The Telcordia Phonetic Pronunciation Package, which also includes the name dictionary  30 . Alternatively, the name dictionary  30  and Rules  35  can be separate.  FIG. 1  illustrates the name dictionary  30  and Rules  35  separate for illustrative purposes only. 
         [0022]    Both the name dictionary  30  and Rules  35  provide the functionality that output multiple pronunciations for the same name The name dictionary  30  is used, for instance, for the purpose of expedience, when the names with different pronunciations do not share many characteristics with each other, as in Koch and Smyth. Different pronunciations are handled by the Rules  35  when, by virtue of relatively small changes in a specific letter-to-sound rule, similar alternate pronunciations can be output for a (possibly large) number of names that share some characteristic, as in “a” in names like Cassani, Christiani, Giuliani, Marchisani, Sobhani, etc. 
         [0023]      FIG. 2  illustrates an exemplary method for customizing voice communication in accordance with the invention. At step  200 , a call is received by the communications device  10 . However, although  FIG. 2  shows a method where a person initiates the call into the voice communication system  1 , the voice communication system  1  can initiate the call. If the voice communication system  1 , initiates the call, step  200  is replaced with initiate a call. (Steps  205 - 220  would be eliminated). The ID for the caller would be known since the voice communication system  1  initiated the call. Additionally, the user file and user profile would also be known. 
         [0024]    At step  205 , the voice communication system  1  determines the identifier for the caller. The identifier can be a caller ID, obtained via automated number identification (ANI), dialed number information service (DNIS) or by prompting the user for an account number or account identifier. 
         [0025]    At step  210 , the processor  40  determines if there is a user file associated with the identifier of the caller. If there is a file (“Y” at step  210 ), the file is retrieved from the user profile storage  25  at step  220 . If there is no file (“N” at step  210 ), the person is redirected to an operator at step  215 . Alternatively, the person can be prompted to re-enter the account number. 
         [0026]    At step  225 , the processor  40  obtains a text spelling of the person&#39;s name or address from the user profile in the user file. The name dictionary  30  is checked to see if at least one pronunciation is associated with the person&#39;s name at step  230 . If there is no available pronunciation (“N” at step  230 ), Rules  35  is consulted at step  235 . However, if there is at least one pronunciation, the available pronunciations are retrieved for comparison with a sample of the person&#39;s speech at step  240 . As described above, the available pronunciations can be ranked by commonality and grouped by tier. Initially, the processor  40  can retrieve only the first tier pronunciations for comparison. 
         [0027]    At step  245 , a speech sample is analyzed. The processor  40  prompts the person or user to say his or her full name or address. The name and/or address capture can be explicit or covert, as when requesting a shipping location for a product or service. Alternatively, the processor  40  can ask the user to confirm his/her identity by asking a secret question. The sample is evaluated/analyzed using the methods described above for the phonetic speech analyzer  20  over the sample period and outputs the phonetic classes for each point in time. As depicted in  FIG. 2 , steps  225 - 240  occur prior to step  245 , however, the order can be reversed. 
         [0028]    At step  250 , the output phonetic classes are compared with either the available pronunciations from the name dictionary  30  or the pronunciation(s) created in step  235  from the Rules  35 . 
         [0029]    The voice communication system  1  via the processor  30 , selects a pronunciation for use based upon the comparison. The selected pronunciation is set as the pronunciation for subsequent interactions. At step  255 , the processor  40  determines if there is a match with one of the available pronunciations. A match is defined using a speech recognition distance determined and a distance threshold. The distance is the difference between an available pronunciation (from either steps  240  or  235 ) and the analyzed speech sample in the form of the phonetic classes. The distance threshold is a parameter that can be set by an operator of the voice communication system  1 . The distance threshold is an allowable deviation or tolerance. Therefore, even if there is not an exact match, as long as the distance is less than the distance threshold, the pronunciation can be used. The larger the distance threshold is, the greater the acceptable deviation is. If the processor  40  determines that there is no match (“N” at step  255 ), i.e., recognition distance is above the distance threshold, there is no reliable match found and a second pass through the name dictionary  30  occurs or a different pronunciation is created from the pronunciations rules storage  35  at step  260 . The second pass through the name dictionary  30  will result in the retrieved pronunciations from the first and later tiers for comparison, i.e., more alternative pronunciations are retrieved. Additionally, more alternatives are created using the Rules. The comparison is repeated (step  250 ) until a reliable match is found, i.e., recognition distance is below the distance threshold (“Y” at step  255 ). 
         [0030]    Once a reliable match is found (“Y” at step  255 ), the pronunciation is set at step  265  and is included in the user profile and stored in the user profile storage  25 . During any subsequent interaction of the user or person with the voice communication system  1 , the pronunciation contained in the user profile is sent to the text-to-speech converter  45 . Additionally, the pronunciation can be used to select from a database of stored speech patterns and phrases. In effect, the voice communication system  1 , will pronounce the name the same way the user does. 
         [0031]    While  FIG. 2  illustrates a method for customizing the pronunciation of a user&#39;s name, the method can be used to customize the pronunciation of other words, such as, but not limited to, regional pronunciations of an address. 
         [0032]    The use of the voice communication system  1  to personalize service interactions with a person such as a user will lead to a) more user satisfaction with the provider company, higher “take” rates (e.g., for offers to participate in automated town halls and robocalls), higher trust of service provider, higher user compliance, and an increased ease-of-use (e.g., for apartment security). 
         [0033]      FIG. 3  illustrates a second exemplary voice communication system  1   a  in accordance with the invention. 
         [0034]    The voice communication system  1   a  allows for the interactions with users to be adapted to individual users by analyzing their speech patterns (speaking style, word choice and dialect). This information can be stored for present or future use, updated based on subsequent interactions and used to direct a text-to-speech and/or interactive voice response system in word and phrase choice, pronunciation and recognition. 
         [0035]    The second exemplary voice communication system  1   a  is similar to the voice communication system  1  described above and common or similar components will not be described again in detail. 
         [0036]    The second exemplary voice communication system  1   a  includes a communications device  10   a,  a phonetic speech analyzer  20   a,  processor  40   a  and a text-to-speech converter  45   a.  Additionally, the second exemplary voice communication system  1   a  includes a user profile storage  25   a  and a dialect database  50  (instead of a name dictionary  30  and pronunciations rules storage  35 ). 
         [0037]    The user profile stored in the user profile storage  25   a  is similar to the profile stored in user profile storage  25 , however, the user profile includes additional speech profile information such as, but not limited to, a selected dialect for recognition and synthesis, a word-choice table, and other speech related information. The user account can include multiple parties within the user file. For example, if an account belongs to a family, a wife and husband would both be included in the file and a personal profile for each will be included in the user profile. 
         [0038]    Table 1 illustrates an example of a portion of the user profile which depicts the speech profiles for a user: 
         [0000]    
       
         
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 User Acct 
                 TTS Dialect 
                 ASR Dialect 
                 Word Choice 
               
               
                   
                 ID 
                 Class 
                 Class 
                 Table 
               
               
                   
                   
               
             
             
               
                   
                 546575 
                 New England 
                 New England 
                 User1 
               
               
                   
                   
               
             
          
         
       
     
         [0039]    The illustrated dialect shown in Table 1 is only for exemplary purposes, and uses a regional description. However, a more detailed dialect description, describing how a user pronounces individual letters or phonemes, could also be used. 
         [0040]    The TTS dialect class is the dialect used for voice recognition of the user. The ASR dialect class is the dialect used for generating a synthesized voice. The dialects for the recognizer and synthesizer can be different. A word choice table includes a list of words or phrases which the user typically substitutes for a standard or common word or phrase. The word choice table is regularly updated based on the user&#39;s speech. After each interaction with the user, the voice communication system  1   a  analyzes the user&#39;s speech and updates the word choice table based upon the words the user spoke. 
         [0041]    Table 2 illustrates an exemplary word choice table: 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                   
               
               
                 Word Choice Table: User1 
               
             
          
           
               
                   
                 Standard Word 
                 Replacement 
               
               
                   
                   
               
               
                   
                 Submarine Sandwich 
                 Hoagie 
               
               
                   
                   
               
             
          
         
       
     
         [0042]    The processor  40   a  is programmed with a program which causes it to perform at least the methods described in  FIGS. 4-6 . 
         [0043]    The phonetic speech analyzer  20   a  is adapted to analyze a speech sample to classify the speech into a dialect from speaking style, word choice and phoneme characteristics. 
         [0044]    The dialect database  50  includes a list of pre-defined set of dialects indexed by name. All of the attributes for each dialect are included in the dialect database. The attributes are continuously updated based upon the voice communication system  1   a  interaction with people. Additionally, new dialects can be added based upon common differences among the users (people) which the voice communication system  1   a  interacts. The dialect can be based upon country and region, such as California, rural Appalachian, southern urban, New England and the like. 
         [0045]      FIG. 4  illustrates a flow chart for customized voice communication in accordance with the invention. Steps  400 - 420  are similar to the steps described in  FIG. 2  (steps  200 - 220 ) and will not be described herein again. Similarly, although  FIG. 4  illustrates that the call is received by the system  1   a,  the voice communication system  1   a  can initiate the call. If the voice communication system  1   a  initiates the call, step  400  is replaced with initiate a call (steps  405 - 420  would be eliminated). The ID for the caller would be known since the voice communication system  1   a  initiated the call. Additionally, the user file and user profile would also be known. 
         [0046]    At step  425 , the processor  40   a  determines if the user profile includes a speech profile. The speech profile includes the dialect, word choice and common user pronunciations. If the user profile does not include a speech profile (“N” at step  425 ), the method proceeds to step  500 , where a speech profile is created. The creation of the speech profile will be described in detail later with respect to  FIG. 5 . 
         [0047]    If the user profile does include a speech profile (“Y” at step  425 ), the phonetic speech analyzer  20   a  analyzes a sample of the user&#39;s speech at step  427  to classify a dialect at step  430 . The analysis and classification is based upon style, word choice, and phoneme characteristics. In particular, the analysis examines speech characteristics and features most useful to distinguish between dialect classes. Typically, speech recognition involves methods of acoustic modeling, (e.g., HMMs of cepstral coefficients) and language modeling (e.g., finding the best matching words in a specified grammar by means of a probability distribution). In this case, the analysis is focused on specific speech features that distinguish dialect classes, e.g., pronunciation and phonology (word accent), prosody/intonation, vocabulary (word choice), and grammar (word order). 
         [0048]    At step  435 , the processor  40   a  determines the number of users or speech profiles that are included in the subject user profile. As noted above, a given user profile can include speech profiles for a family. 
         [0049]    If there is only one speech profile in the user profile (“N” at step  435 ), the dialect in the speech profile is compared with the classified dialect from the sample speech at step  440 . If there is a match (“Y” at step  440 ), the speech profile is used for subsequent voice communication at step  445 . If there is no match (“N” at step  440 ), then the difference is evaluated at step  475 . The attributes of the speech sample are directly compared with the attribute of the stored dialect from the speech profile using the dialect database  50  to determine a recognition distance. The distance is compared with a tolerance or a distance threshold at step  480 . The distance threshold is a parameter that can be set by an operator of the voice communication system  1   a.  The distance threshold is an allowable deviation or tolerance. Therefore, even if there is not an exact match, then as long as the distance is less than the distance threshold, the dialect can be used. The larger the distance threshold is, the greater the acceptable deviation is. As long as any differences are minor, i.e., less than the distance threshold (“N” at step  480 ), the pre-set dialect can still be used (step  445 ). The user profile is updated to record these differences at step  485 . The differences are recorded for subsequent analysis both for a particular user and across users. This analysis will be described later in detail with respect to  FIG. 6 . If the differences are word choice and pronunciations, the word choice table and pronunciation can also be updated at step  485 . If at step  480  the differences are significant (“Y” at step  480 ), a new speech profile is created. The method proceeds to step  505 . 
         [0050]    If there are more than one speech profile or user (“Y” at step  435 ), the classified dialect from the speech sample is compared with the dialects from each of the speech profiles to determine a match at step  450 . For each match, the processor  40   a  in combination with phonetic speech analyzer  20   a  confirms that the actual caller is one of the users that had a dialect match, i.e., the right person at step  455 . This is done by examining the speech characteristics, such as, but not limited to, speaking rate, pitch range, gender, spectrum and estimates of the speakage&#39;s age using the speech pattern. 
         [0051]    At step  460 , the processor  40   a  determines if there is a match, i.e., the person speaking is on the account and matches the classified dialect. If there is a match for one of the users, the speech profile is used for subsequent voice communication at step  445 . If no match is found, at step  460 , either a new user profile can be created, i.e., method proceeds to step  505  or an error can be announced. If at step  450 , the classified dialect does not match any of the stored dialect on the speech profiles (any user associated with the account) (“N” at step  450 ), the method moves to step  490  and the difference is evaluated. The difference is evaluated for each speech profile (each user associated with the account) in the same manner as described above. The attribute associated with the dialects from the speech profile are compared with the attributes of the sample speech. If the difference for each of the dialects from the speech profile is greater than the tolerance (“Y” at step  492 ), than a speech profile is created starting with step  505 . The speech profile having the smallest difference between the dialect and the sample speech will be selected at step  495  for further analysis, i.e.; process will move to step  455 . 
         [0052]    During the subsequent portion of the dialog, the phonetic speech analyzer  20   a  regularly monitors the speech for changes in the speech profile at step  465 . Updates to the profile may include modification of word choice (does user say “hero”, “sub”, “hoagie” etc.) or updates to the user&#39;s pronunciation of works (tomato with a long or short “a” sound). The speech profile is updated based upon these changed at step  470 . 
         [0053]      FIG. 5  illustrates a method for creating a speech profile according to the invention. Step  500  is performed when a new user contacts the system  1   a.  This step is equivalent to step  430  and will not be described again in detail. Step  500  can be omitted if a speech sample has been already analyzed. At step  505 , a word-choice table is created for the user. Table 2 is an example of the word-choice table. Initially, the word-choice table is based upon a region or location of the user and is defined by the dialect. However, as noted above, the word-choice table is regularly updated based upon the interaction with the user. Similarly, at step  505 , a special-pronunciation dictionary is created based upon the dialect, i.e., initialized. Like the word-choice table, the special-pronunciation dictionary is also regularly updated based upon the interaction with the user. At step  510 , a system operator can choose whether the classified dialect is to be used for both recognition and synthesis. The default can be that the dialect is used for both. If the dialect is used for both recognition and synthesis (“Y” at step  510 ), the processor  40   a  set the classified dialect for both at step  515  and the dialect, word-choice table and special pronunciation are stored in the speech profile in the user profile at step  525 . If the dialect is not used for both the recognition and synthesis (“N” at step  510 ), the dialects are separately set at step  520 . 
         [0054]      FIG. 6  illustrates a method for updating and creating new dialects based upon common difference in accordance with the invention. 
         [0055]    At step  600 , the difference information is retrieved from each of the speech profiles, along with the actual assigned dialects. The differences are evaluated for patterns and similarities across multiple users (with both the same and different dialects) at step  605 . If the differences are significant, i.e., greater than an allowable tolerance, a new dialect can be created. At step  610 , the common differences are evaluated by magnitude. If the differences are greater than the tolerance (“Y” at step  610 ) a new dialect is created with attributes including the common differences at step  615 . The dialect database  50  is updated. 
         [0056]    If the common difference is less than the tolerance, a determination is made if users have the same dialect. If the analysis across multiple users map to the same dialect indicates a common difference between multiple users and the dialect (“Y” step  620 ), the defined dialect can be updated at step  625 . The dialect database  50  is updated to reflect the change in the attributes of the existing dialect. 
         [0057]    If the differences are not significant and not for the same dialect (e.g., random), then the dialect remains the same at step  630 . The individually customized speech profile is still updated to account for the differences on an individual level. The process is repeated for all of the dialects that have difference information. 
         [0058]    Alternatively, the dialect differences could be learned via clustering techniques or other means of machine learning. In this approach, dialect differences for user A could be expanded by identifying similarities to other users and updating user A&#39;s profile with entries from the similar profiles. 
         [0059]    The features of the voice communication system  1   a  can be selectively enabled or disabled on an individual basis. An operator of the system can select certain features to enable. For example, the choice of dialect to use can also be made selectively. Users with strong accents or unusual dialects might take offense at a system that appears to be imitating them. Additionally, the pre-defined dialects can be defined to avoid pronunciations that users might find insulting. Furthermore, during the updating process which has been described herein, updates to pronunciation can be limited to a defined set that has been vetted by system operators. For example, a user with a German accent speaking English might pronounce “water” with an initial “V” sound. The voice communication system  1   a  can be configured to avoid using this pronunciation as part of the defined set for speech synthesis. A person from New England might pronounce “water” with no final “R” sound. This voice communication system  1   a  can be configured to include this pronunciation in the defined set for synthesis. Thus, in this example, the voice communication system  1   a  can update the pronunciation of water for the user from Boston, but would not update the pronunciation for the user with a German accent. 
         [0060]    As described herein, the pronunciation dialect that is used for recognition can be separately controlled or updated from the dialect used for speech synthesis. Therefore, the dialects can be different. In the above example, updating the recognition pronunciation of “water” for the native German speaker would improve recognition accuracy. Thus the two pronunciation lexicons can be separated to improve overall system performance, as shown in Table 1. 
         [0061]    Additionally, to make the transition appear more seamless to the user, any significant change(s) in dialect could also be accompanied by a change in voice, such as from male to female. Advantageously, this would give the user the impression that they were transferred to an individual with the appropriate language capabilities. These impressions could be enhanced with a verbal announcement to that effect. 
         [0062]    Various aspects of the present disclosure may be embodied as a program, software, or computer instructions embodied or stored in a computer or machine usable or readable medium, which causes the computer or machine to perform the steps of the method when executed on the computer, processor, and/or machine. A computer readable medium, tangibly embodying a program of instructions executable by the machine to perform various functionalities and methods described in the present disclosure is also provided. 
         [0063]    The systems and methods of the present disclosure may be implemented and run on a general-purpose computer or special-purpose computer system. The computer system may be any type of known or will be known systems and may typically include a processor, memory device, a storage device, input/output devices, internal buses, and/or a communications interface for communicating with other computer systems in conjunction with communication hardware and software, etc. 
         [0064]    The computer readable medium could be a computer readable storage medium (device) or a computer readable signal medium. Regarding a computer readable storage medium, it may be, for example, a magnetic, optical, electronic, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing; however, the computer readable storage medium is not limited to these examples. Additional particular examples of the computer readable storage medium can include: a portable computer diskette, a hard disk, a magnetic storage device, a portable compact disc read-only memory (CD-ROM), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an electrical connection having one or more wires, an optical fiber, an optical storage device, or any appropriate combination of the foregoing; however, the computer readable storage medium is also not limited to these examples. Any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device could be a computer readable storage medium. 
         [0065]    The terms “computer system”, “system”, “computer network” and “network” as may be used in the present disclosure may include a variety of combinations of fixed and/or portable computer hardware, software, peripherals, and storage devices. The computer system may include a plurality of individual components that are networked or otherwise linked to perform collaboratively, or may include one or more stand-alone components. The hardware and software components of the computer system of the present disclosure may include and may be included within fixed and portable devices such as desktop, laptop, and/or server. A module may be a component of a device, software, program, or system that implements some “functionality”, which can be embodied as software, hardware, firmware, electronic circuitry, or etc. 
         [0066]    The embodiments described above are illustrative examples and it should not be construed that the present invention is limited to these particular embodiments. Thus, various changes and modifications may be effected by one skilled in the art without departing from the spirit or scope of the invention as defined in the appended claims.