Speech Recognition Dialog Management

• US 20090018829A1
• Filed: 06/08/2005
• Published: 01/15/2009
• Est. Priority Date: 06/08/2004
• Status: Abandoned Application

• Current Assignee: Metaphor Solutions, Inc.
• Original Assignee: Metaphor Solutions, Inc.
• Inventors: Kuperstein, Michael
• Application Number:
  US11/629,034
• Publication Number:
  US 20090018829A1
• Time in Patent Office: Days
• US Class Current: 704/235
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 2015/0631 Creating reference templates; Clustering
• CPC Class Codes: G10L 2015/228 of application context

Abstract

Described is a speech recognition dialog management system that allows more open-ended conversations between virtual agents and people than are possible using just agent-directed dialogs. The system uses both novel dialog context switching and learning algorithms based on spoken interactions with people. The context switching is performed through processing multiple dialog goals in a last-in-first-out (LIFO) pattern. The recognition accuracy for these new flexible conversations is improved through automated learning from processing errors and addition of new grammars.

Claims

1. A method of flexible dialog management in a speech recognition system, the method comprising:

receiving a spoken utterance from a user during an automated conversation between the user and a virtual agent;

attempting to recognize the spoken utterance with a phrase in an existing speech grammar;

if the spoken utterance fails to match a phrase in the speech grammar, resulting in a speech matching error, then processing the speech matching error by updating the speech grammar within one or more meaning categories to include an additional phrase that corresponds to a part or all of the spoken utterance.

2. The method of claim 1 wherein updating the speech grammar further comprises:

transcribing an audio recording of the spoken utterance to a textual representation;

semantically analyzing the textual representation to determine a meaning category corresponding to the textual representation;

mapping the textual representation to one or more of a predetermined set of meaning categories; and

adding a part or all of the textual representation of the spoken utterance in the corresponding meaning categories to the speech grammar.

3. The method of claim 2 wherein the speech grammar includes a focus grammar and an orienting grammar, the focus grammar being used to recognize one or more of expected responses mapped to one or more of expected meaning categories to a prompt from the virtual agent during the automated conversation with the user, the orienting grammar being used to recognize one or more of a set of questions or topic changes not covered by the focus grammar but related to the automated conversation.

4. The method of claim 3 wherein the textual representation of part or all of the unrecognized spoken utterance is added either to the focus grammar if the one or more meaning categories associated with the unrecognized spoken utterance corresponds to a current focus of the automated conversation at the time of the speech matching error or to the orienting grammar if the meaning category associated with the unrecognized spoken utterance corresponds to one or more meaning categories associated the orienting grammar.

5. A speech recognition system with flexible dialog management, said system comprising:

a communication interface receiving an utterance from a user during an automated conversation between the user and a virtual agent;

a stored speech grammar;

a speech recognition module attempting to recognize the utterance with a phrase in the stored speech grammar;

a learning module processing a speech matching error in case of a failure in matching a phrase in the stored speech grammar by updating the stored speech grammar within one or more meaning categories to include an additional phrase that corresponds to a part or all of the utterance.

6. The speech recognition system of claim 5, wherein the learning module further comprises:

a transcriber transcribing an audio recording of the utterance to a textual representation;

a semantic analyzer analyzing the textual representation to determine a meaning category corresponding to the textual representation;

a mapping of the textual representation to one or more of a predetermine set of meaning categories; and

a new speech grammar comprising the stored speech grammar and an added part or all of the textual representation of the utterance in the corresponding meaning category.

7. The speech recognition system of claim 6, wherein the stored speech grammar includes a focus grammar and an orienting grammar, the focus grammar being used to recognize one or more of expected responses mapped to one or more of expected meaning categories to a prompt from the virtual agent during the automated conversation with the user, the orienting grammar being used to recognize one or more of a set of questions or topic changes not covered by the focus grammar but related to the automated conversation.

8. The speech recognition system of claim 7, wherein the textual representation of part or all of the unrecognized utterance is added either to the focus grammar if the one or more meaning categories associated with the unrecognized spoken utterance corresponds to a current focus of the automated conversation at the time of the speech matching error or to the orienting grammar if the meaning category associated with the unrecognized spoken utterance corresponds to one or more meaning categories associated the orienting grammar.

9. A content readable medium storing instructions for flexible dialog management in a speech recognition system, said instructions comprising:

instructions for receiving a spoken utterance from a user during an automated conversation between the user and a virtual agent;

instructions for attempting to recognize the spoken utterance with a phrase in an existing speech grammar;

instructions for, if the spoken utterance fails to match a phrase in the speech grammar, resulting in a speech matching error, then processing the speech matching error by updating the speech grammar within one or more meaning categories to include an additional phrase that corresponds to a part or all of the spoken utterance.

10. A method of flexible dialog management in a speech recognition system, the method comprising:

conducting an automated conversation between a user and a virtual agent according to a first script to satisfy a first goal associated with a meaning category of a speech grammar;

receiving a spoken utterance from the user;

attempting to recognize the spoken utterance with a phrase in a focus grammar and an orienting grammar, the focus grammar being used to recognize one of responses to a prompt from the virtual agent, the orienting grammar being used to recognize one of a set of questions or topic change commands not covered by the focus grammar but related to a subject of the automated conversation;

if the recognized utterance matches a phrase in the orienting grammar, storing the first script for the automated conversation in memory;

determining a second goal associated with the matched phrase in the orienting grammar;

conducting the automated conversation between the user and the virtual agent according to a second script to satisfy the second goal.

11. The method of claim 10 further comprising:

after satisfying the second goal, querying the user whether to continue processing the first script; and

if so, retrieving the first script for the conversation from the memory; and

continuing to conduct the automated conversation between the user and the virtual agent according to the first script to satisfy the first goal.

12. The method of claim 10 wherein if the recognized utterance matches a phrase in the focus grammar, the method further comprises:

continuing to conduct the automated conversation between the user and the virtual agent according to the first script to satisfy the first goal.

13. The method of claim 10 wherein the speech grammar is a finite state grammar or a statistical language model grammar.

14. A speech recognition system with flexible dialog management, said system comprising:

an application conducting an automated conversation between a user and a virtual agent according to a first script to satisfy a first goal associated with a meaning category of a speech grammar;

focus grammar used to recognize one of responses to a prompt from the virtual agent;

orienting grammar used to recognize one of a set of questions or topic change commands related to a subject of the automated conversation;

a communication engine receiving a spoken utterance from the user; and

if the received spoken utterance matches a phrase in the orienting grammar, said system further comprising;

a memory storing the first script for the automated conversation if the received spoken utterance matches a phrase in the orienting grammar;

the application conducting the automated conversation between the user and the virtual agent according to a second script to satisfy a second goal.

15. The system of claim 14, wherein the speech grammar is a finite state grammar or a statistical language model grammar.

16. A content readable medium storing instructions for flexible dialog management in a speech recognition system, said instructions comprising:

instructions for conducting an automated conversation between a user and a virtual agent according to a first script to satisfy a first goal associated with a meaning category of a speech grammar;

instructions for receiving a spoken utterance from the user;

instructions for attempting to recognize the spoken utterance with a phrase in a focus grammar and an orienting grammar, the focus grammar being used to recognize one of responses to a prompt from the virtual agent, the orienting grammar being used to recognize one of a set of questions or topic change commands related to a subject of the automated conversation;

if the recognized utterance matches a phrase in the orienting grammar,

instructions for storing the first script for the automated conversation in memory;

instructions for determining a second goal associated with the matched phrase in the orienting grammar;

instructions for conducting the automated conversation between the user and the virtual agent according to a second script to satisfy the second goal.

17. The content readable medium of claim 16, further comprising:

instructions for, after satisfying the second goal, querying the user whether to continue processing the first script; and

if so,

instructions for retrieving the first script for the conversation from the memory; and

instructions for continuing to conduct the automated conversation between the user and the virtual agent according to the first script to satisfy the first goal.

18. The content readable medium of claim 16 wherein if the recognized utterance matches a phrase in the focus grammar, the instructions further comprise:

instructions for continuing to conduct the automated conversation between the user and the virtual agent according to the first script to satisfy the first goal.

19. The content readable medium of claim 16 wherein the speech grammar is a finite state grammar or a statistical language model grammar.