Abstract:
The present disclosure relates to prompting for a spoken response that provides input for multiple elements. A single spoken utterance including content for multiple elements can be received, where each element is mapped to a data field. The spoken utterance can be speech-to-text converted to derive values for each of the multiple elements. An utterance level confidence score can be determined, which can fall below an associated certainty threshold. Element-level confidence scores for each of the derived elements can then be ascertained. A first set of the multiple elements can have element-level confidence scores above an associated certainty threshold and a second set can have scores below. Values can be stored in data fields mapped to the first set. A prompt for input for the second set can be played.

Description:
BACKGROUND 
     1. Field of the Invention 
     The present invention relates to the field of speech processing, and, more particularly, to partially filling mixed-initiative forms from utterances having confidence scores below a threshold based upon word-level confidence data. 
     2. Description of the Related Art 
     VoiceXML documents define applications as a set of named dialog states. The user is always in one dialog state at any time. Voice XML dialogs include forms and menus. A form defines an interaction that collects values for each of a set of fields in the form. Each field can specify a prompt, the expected input, and evaluation rules. Additionally, each dialog state has one or more grammars associated with it that are used to describe the expected user input which includes spoken input and/or touch-tone (DTMF) key presses. 
     Two means are commonly used to gather data to fill multiple form items. One means to gather data assigns a specific grammar to each form item and utilizes a Form Interpretation Algorithm (FIA) to visit each form item until each one is filled with data provided by a user. The second means collects multiple pieces of information in a single dialog state. This type of form is a mixed-initiative form associated with a form-level grammar. 
     Since a form-level grammar supports filling multiple fields, it is more complex and the associated speech utterances are longer than utterances associated with filling a single field. Longer utterances have a relatively high probability of returning NO_MATCH results and in being incorrectly recognized by a speech recognition engine. Each recognized utterance is typically associated with an utterance-level (e.g., a form-level or phrase-level) confidence score. When this utterance-level confidence score is below a designated confidence threshold, a user will typically be re-prompted for the full utterance in hopes that a new utterance will result in a higher confidence score. Being forced to repeat a complete utterance can be time consuming and frustrating to user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       There are shown in the drawings, embodiments which are presently preferred, it being understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown. 
         FIG. 1  is a schematic diagram of a system for partially filling mixed-initiative forms when a confidence score of an utterance prevents all fields of the mixed-initiative form from being filled. 
         FIG. 2  is a diagram illustrating a use of a system able to partially fill mixed-initiative forms in accordance with an embodiment of the inventive arrangements disclosed herein. 
         FIG. 3  provides code for a sample mixed-initiative form and a sample grammar document, where the form is able to be partially filled in based upon word confidence scores in accordance with an embodiment of the inventive arrangements disclosed herein. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a schematic diagram of a system  100  for partially filling mixed-initiative forms when a confidence score of an utterance prevents all fields of the mixed-initiative form from being filled. More specifically, system  100  determines word-level confidence scores for each word in a spoken phrase  114 . When the phrase  114  has an insufficient phrase confidence score to be used to determine values for a complete set of fields, a portion of the fields (e.g., those associated with one or more words recognized with a high confidence) in the set can be completed. A speaker can then be prompted  112  to provide values for the unfilled fields only. 
     System  100  can include an application server  120  executing a speech-enabled application  122 . The application  122  can interact with a user  110  using a voice-only interface and/or multimodal interface that accepts voice input and presents voice output. The speech-enabled application  122  can be written in a voice markup language (e.g., Voice XML) or a hybrid markup language (e.g., X+V) that has voice handling capabilities. Additionally, the application  122  can have interface routines written in one programming language (e.g., JAVA or C) and can use language code written in voice markup language to handle speech related tasks. 
     One or more mixed-initiative forms  132  can be associated with user fillable fields of the speech-enabled application. For example, a mixed initiative form  132  can be associated with a residence address of a customer, where the residence address includes fields for street, city, state, and zip code as shown by table  124 . Additionally, each mixed-initiative form  132  can be associated with a grammar document  134 . The grammar document  134  can specify a fixed format grammar, such as an Extensible Markup Language (XML) Speech Recognition Grammar Specification (SRGS) based grammar or an augmented Backus-Naur form (ABNF) based grammar. 
     A speech processing system  140  having an automated speech recognition (ASR) engine  142  can perform speech recognition tasks for system  100 . The speech processing system  140  can match a user provided utterance  114  against an associated grammar specified by grammar document  134 . Matching the utterance  114  involves first producing parse-tree, which can be considered an instance of an active recognition grammar. When system  140  produces recognition results  150  for the utterance  114 , it can include an utterance match and an associated utterance (phrase-level) confidence score. 
     The phrase-level score is mathematically generated by combining confidence scores associated with branches of the parse-trees. One or more of these branches can be an intermediate branch, which has multiple leaves, each leaf having a recognized word and an associated confidence score. A confidence score for the intermediate branch can be a mathematical result based upon its leaf nodes. Further, each work in the utterance can have an associated word-level confidence score. It should be noted that some commercial speech processing systems  140  (e.g., IBM Embedded Speech Recognizer) are able to automatically provide a confidence score for each word in a recognized utterance. 
     When an utterance confidence score of result  150  is less than an associated phrase-level threshold, results  150  are believed to be too uncertain to complete all entries in the associated mixed-initiative form  132 . System  100  can, however, use the word level confidence scores to fill-in a portion of the fields. That is, word level confidence scores can propagate up a parse-tree to branch nodes, each of which is associated with a field. When a branch node confidence score is greater than a branch-level threshold, the associated field can be completed, which is represented by a conveyance of a partial document  152 . Partial document  152  can be used to establish values for a partial set of the fields associated with a mixed-initiative form  132 . For example, values for city, state, and zip can be contained in partial document  152  in a situation where street has too low of a branch-level confidence score. The user  110  will be re-prompted  154  to complete the unfilled fields. 
     It should be appreciated that different grammar weights can be applied to branches of the parse-tree. These grammar weights can be configurable by an authorized administrator, by application  122  routine and/or by the user  110 . Configuring the grammar weights affects a manner in which the phrase-level and branch-level confidence score is generated. For example, the city field can be more heavily weighted than the state filed when determining a phrase-level confidence score for a full address. In another example, a street name and number can be more heavily weighed than a street type when determining a branch-level confidence score for a street field. 
       FIG. 2  is a diagram  200  illustrating a use of a system (e.g., system  100 ) able to partially fill mixed-initiative forms in accordance with an embodiment of the inventive arrangements disclosed herein. In diagram  200 , a voice response system can prompt  210  a user to provide a street, city, state, and zip code. The customer can speak their response  212 , which in the example is “8051 Congress Avenue, Boca Raton, Fla. 33487”. 
     A parse-tree  220  can be generated, which is an instance of a grammar corresponding to a mixed-initiative form for the full address. The parse tree  220  can include a root node of U.S. Address  222 , with branch nodes  224  of street, city, state, and zip code. The street node  224  can include branching nodes  226  for street number, street name, and street type. A speech recognition engine can match values  228  to each of the branching nodes  226 . A form-level (e.g., phrase-level) confidence scores for the U.S. Address  222  node can be less than a previously designated confidence threshold, which initiates that the spoken utterance  212  has not been recognized with sufficient confidence to completely fill fields of the multi-initiative form for which the prompt  210  was directed. 
     During a matching phrase for the utterance  210 , a speech recognition engine can determine confidence scores for each spoken work of the utterance  210 . For example, word confidence scores  240  can be generated as follows: “8051”-60/100; “Congress”-30/100; “Avenue”-60/100; “Boca”-80/100; “Raton”-60/100; “Florida”-70/100; and “33487” 80/100. In this example, confidence scores are indicated as values 0-100, with 100 representing a score of 100% confidence. 
     Parse-tree  250  includes one contemplated algorithm for applying word-level confidence scores to node values. Here, a lowest confidence score of a child node is iteratively propagated upward to a parent node. That is, since a street node  252  is a parent of a number node  254 , a name node  256 , and a type node  258 , the lowest confidence score for nodes  254 - 258  (e.g., confidence score of 30/100 for the name node) is propagated upwards to the street node  252 . 
     Assuming a branch-level confidence threshold is established at 50/100, the street node  252  (e.g., confidence score of 30/100) falls below this threshold, while all other branch level nodes exceed the branch-level threshold (e.g., city-60/100, state-70/100”, and zip code-80-100). Consequently, a city field can be filled in with “Boca Raton”, a state field can be filled in with “Florida”, and a zip code field can be filled in with “33487”. A user will be re-prompted to speak or otherwise re-input a street address. 
     It should be appreciated that the above example is for one potential algorithm for applying word-level confidence scores (also called a percolation algorithm) to node values and that the invention is not to be construed as limited in this regard. The precolation algorithm can be a configurable algorithm, which can be customer defined or vendor provided. Further in various contemplated embodiment, the precolation algorithm can utilize a maximum value from nodes at the same level, can utilize an average value, can utilize a statistical mean value, and the like. 
       FIG. 3  provides a code for a sample mixed-initiative form  310  and a sample grammar document  340 , where the form is able to be partially filled in based upon word confidence scores in accordance with an embodiment of the inventive arrangements disclosed herein. Code  310  and  340  can be performed in the context of system  100 . Code  310  and  340  is consistent with examples provided in diagram  200 . Form  310  can be written in any language that is able to be interpreted by a Voice server. As shown, form  310  is written in VoiceXML. Similarly, the grammar document  340  can be any fixed format grammar, such as an XML SRGS based grammar on an ABNF based grammar. 
     Form  310  is linked to an associated grammar called us_address.gram  312 . The form  310  prompts a user for a full address  314  including street  316 , city  318 , state  320 , and zip code  322 . 
     As shown, grammar  340  is a ABNF grammar specified by the file, us_address.gram. Line  342  (e.g., public $us_address=$street $city $state $zipcode) indicates that street, city, state, and zipcode are subrules of the grammar  340 . After the subrule-declaration, semantic interpretation statement  344  sets the results of each sub-rule to an associated Voice XML field (e.g., $.street=$street; $.city=$city; $.state=$state; $.zipcode=$zipcode). 
     The present invention may be realized in hardware, software, or a combination of hardware and software. The present invention may be realized in a centralized fashion in one computer system, or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software may be a general purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein. 
     The present invention also may be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which when loaded in a computer system is able to carry out these methods. Computer program in the present context means any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: a) conversion to another language, code or notation; b) reproduction in a different material form. 
     This invention may be embodied in other forms without departing from the spirit or essential attributes thereof. Accordingly, reference should be made to the following claims, rather than to the foregoing specification, as indicating the scope of the invention.