Method and apparatus for providing speech-driven routing between spoken language applications
An apparatus and a concomitant method for speech recognition. In one embodiment, a distributed speech recognition system provides speech-driven control and remote service access. The distributed speech recognition system comprises a client device and a central server, where the client device is equipped with two speech recognition modules: a foreground speech recognizer and a background speech recognizer. The foreground speech recognizer is implementing a particular spoken language application (SLA) to handle a particular task, whereas the background speech recognizer is monitoring a change in the topic and/or a change in the intent of the user. Upon detection of a change in topic or intent of the user, the background speech recognizer will effect the routing to a new SLA to address the new topic or intent.
1. Method for performing speech recognition, said method comprising the steps of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of topic; and
(d) forwarding a second language model to said first speech recognizer in response to said detected change of topic by said second speech recognizer.
2. The method of claim 1 , wherein said speech signal is received locally from said user via a client device.
3. The method of claim 2 , further comprising the step of:
(b′) adapting said performance of speech recognition by said first speech recognizer based on at least one local parameter.
4. The method of claim 2 , further comprising the step of:
(c′) forwarding a signal to a central server in accordance with said detected change of topic, where said second language model is provided by said central server.
5. The method of claim 3 , wherein said at least one local parameter is representative of an environmental noise.
6. The method of claim 3 , wherein said at least one local parameter is representative of an acoustic environment.
7. The method of claim 3 , wherein said at least one local parameter is representative of a pronunciation of said user.
8. The method of claim 1 , further comprising the step of:
(e) storing at least a portion of said second language model in a cache of said client device.
9. The method of claim 1 , wherein said second speech recognizer performs said speech recognition step (c) by employing a language model that employs a catch all model.
10. The method of claim 9 , wherein said catch all model is employed to model at least one word that is not defined within a vocabulary of said language model of said second speech recognizer.
11. Method for performing speech recognition, said method comprising the steps of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing topic spotting from said recognizable text signal in accordance with a second speech recognizer for detecting a change of topic; and
(d) forwarding a second language model to said first speech recognizer in response to a detected change of topic by said second speech recognizer.
12. The method of claim 11 , wherein said speech signal is received locally from said user via a client device.
13. The method of claim 12 , further comprising the step of:
(b′) adapting said performance of speech recognition by said first speech recognizer based on at least one local parameter.
14. The method of claim 12 , further comprising the step of:
(c′) forwarding a signal to a central server in accordance with said detected change of topic, where said second language model is provided by said central server.
15. The method of claim 13 , wherein said at least one local parameter is representative of an environmental noise.
16. The method of claim 13 , wherein said at least one local parameter is representative of an acoustic environment.
17. The method of claim 13 , wherein said at least one local parameter is representative of a pronunciation of said user.
18. The method of claim 11 , further comprising the step of:
(e) storing at least a portion of said second language model in a cache of said client device.
19. The method of claim 11 , wherein said second speech recognizer performs said speech recognition step (c) by employing a language model that employs a catch all model.
20. The method of claim 19 , wherein said catch all model is employed to model at least one word that is not defined within a vocabulary of said language model of said second speech recognizer.
21. Method for performing speech recognition, said method comprising the steps of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of topic; and
(d) updating said first language model with a second language model of said first speech recognizer in response to a detected change of topic by said second speech recognizer.
22. The method of claim 21 , wherein said speech signal is received locally from said user via a client device.
23. The method of claim 22 , further comprising the step of:
(b′) adapting said performance of speech recognition by said first speech recognizer based on at least one local parameter.
24. The method of claim 22 , further comprising the step of:
(c′) forwarding a signal to a central server in accordance with said detected change of topic, where said second language model is provided by said central server.
25. The method of claim 23 , wherein said at least one local parameter is representative of an environmental noise.
26. The method of claim 23 , wherein said at least one local parameter is representative of an acoustic environment.
27. The method of claim 23 , wherein said at least one local parameter is representative of a pronunciation of said user.
28. The method of claim 21 , further comprising the step of:
(e) storing at least a portion of said second language model in a cache of said client device.
29. The method of claim 21 , wherein said second speech recognizer performs said speech recognition step (c) by employing a language model that employs a catch all model.
30. The method of claim 29 , wherein said catch all model is employed to model at least one word that is not defined within a vocabulary of said language model of said second speech recognizer.
31. Method for performing speech recognition, said method comprising the steps of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of intent; and
(d) forwarding a second language model to said first speech recognizer in response to said detected change of intent by said second speech recognizer.
32. The method of claim 31 , wherein said speech signal is received locally from said user via a client device.
33. The method of claim 32 , further comprising the step of:
(b′) adapting said performance of speech recognition by said first speech recognizer based on at least one local parameter.
34. The method of claim 32 , further comprising the step of:
(c′) forwarding a signal to a central server in accordance with said detected change of intent, where said second language model is provided by said central server.
35. The method of claim 33 , wherein said at least one local parameter is representative of an environmental noise.
36. The method of claim 33 , wherein said at least one local parameter is representative of an acoustic environment.
37. The method of claim 33 , wherein said at least one local parameter is representative of a pronunciation of said user.
38. The method of claim 31 , further comprising the step of:
(e) storing at least a portion of said second language model in a cache of said client device.
39. The method of claim 31 , wherein said second speech recognizer performs said speech recognition step (c) by employing a language model that employs a catch all model.
40. The method of claim 39 , wherein said catch all model is employed to model at least one word that is not defined within a vocabulary of said language model of said second speech recognizer.
41. Method for performing speech recognition, said method comprising the steps of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first spoken language application to produce a recognizable text signal, wherein said first spoken language application employs a first language model;
(c) performing topic spotting from said recognizable text signal in accordance with a second spoken language application for detecting a change of topic; and
(d) forwarding a second language model to said first spoken language application in response to a detected change of topic by said second spoken language application.
42. The method of claim 41 , wherein said speech signal is received locally from said user via a client device.
43. The method of claim 42 , further comprising the step of:
(b′) adapting said performance of speech recognition by said first spoken language application based on at least one local parameter.
44. The method of claim 42 , further comprising the step of:
(c′) forwarding a signal to a central server in accordance with said detected change of topic, where said second language model is provided by said central server.
45. The method of claim 43 , wherein said at least one local parameter is representative of an environmental noise.
46. The method of claim 43 , wherein said at least one local parameter is representative of an acoustic environment.
47. The method of claim 43 , wherein said at least one local parameter is representative of a pronunciation of said user.
48. The method of claim 41 , further comprising the step of:
(e) storing at least a portion of said second language model in a cache of said client device.
49. The method of claim 41 , wherein said second spoken language application performs said topic spotting step (c) by employing a language model that employs a catch all model.
50. The method of claim 49 , wherein said catch all model is employed to model at least one word that is not defined within a vocabulary of said language model of said second spoken language application.
51. A client device for performing speech recognition, said client device comprising:
means for receiving a speech signal from a user;
means for performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
means for performing a speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of topic; and
means for forwarding a second language model to said first speech recognizer in response to said detected change of topic by said second speech recognizer.
52. A client device for performing speech recognition, said client device comprising:
means for receiving a speech signal from a user;
means for performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
means for performing topic spotting from said recognizable text signal in accordance with a second speech recognizer for detecting a change of topic; and
means for forwarding a second language model to said first speech recognizer in response to said detected change of topic by said second speech recognizer.
53. A client device for performing speech recognition, said client device comprising:
means for receiving a speech signal from a user;
means for performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
means for performing speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of topic; and
means for updating said first language model with a second language model of said first speech recognizer in response to a detected change of topic by said second speech recognizer.
54. A client device for performing speech recognition, said client device comprising:
means for receiving a speech signal from a user;
means for performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
means performing a speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of intent; and
means forwarding a second language model to said first speech recognizer in response to said detected change of intent by said second speech recognizer.
55. A client device for performing speech recognition, said client device comprising:
means for receiving a speech signal from a user;
means for performing speech recognition on said speech signal in accordance with a first spoken language application to produce a recognizable text signal, wherein said first spoken language application employs a first language model;
means for performing topic spotting from said recognizable text signal in accordance with a second spoken language application for detecting a change of topic; and
means for forwarding a second language model to said first spoken language application in response to a detected change of topic by said second spoken language application.
56. A computer-readable medium having stored thereon a plurality of instructions, the plurality of instructions including instructions which, when executed by a processor, cause the processor to perform the steps comprising of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing a speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of topic; and
(d) forwarding a second language model to said first speech recognizer in response to said detected change of topic by said second speech recognizer.
57. A computer-readable medium having stored thereon a plurality of instructions, the plurality of instructions including instructions which, when executed by a processor, cause the processor to perform the steps comprising of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing topic spotting from said recognizable text signal in accordance with a second speech recognizer for detecting a change of topic; and
(d) forwarding a second language model to said first speech recognizer in response to a detected change of topic by said second speech recognizer.
58. A computer-readable medium having stored thereon a plurality of instructions, the plurality of instructions including instructions which, when executed by a processor, cause the processor to perform the steps comprising of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of topic; and
(d) updating said first language model with a second language model of said first speech recognizer in response to a detected change of topic by said second speech recognizer.
59. A computer-readable medium having stored thereon a plurality of instructions, the plurality of instructions including instructions which, when executed by a processor, cause the processor to perform the steps comprising of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first speech recognizer to produce a recognizable text signal, wherein said speech recognizer employs a first language model;
(c) performing a speech recognition on said speech signal in parallel in accordance with a second speech recognizer for detecting a change of intent; and
(d) forwarding a second language model to said first speech recognizer in response to said detected change of intent by said second speech recognizer.
60. A computer-readable medium having stored thereon a plurality of instructions, the plurality of instructions including instructions which, when executed by a processor, cause the processor to perform the steps comprising of:
(a) receiving a speech signal from a user;
(b) performing speech recognition on said speech signal in accordance with a first spoken language application to produce a recognizable text signal, wherein said first spoken language application employs a first language model;
(c) performing topic spotting from said recognizable text signal in accordance with a second spoken language application for detecting a change of topic; and
(d) forwarding a second language model to said first spoken language application in response to a detected change of topic by said second spoken language application.