NAMED-ENTITY BASED SPEECH RECOGNITION

• US 20150088511A1
• Filed: 09/24/2013
• Published: 03/26/2015
• Est. Priority Date: 09/24/2013
• Status: Abandoned Application

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Inventors: Bharadwaj, Sujeeth S., Medapati, Suri B.
• Application Number:
  US14/035,845
• Publication Number:
  US 20150088511A1
• Time in Patent Office: Days
• US Class Current: 704/244
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 2015/0633 using lexical or orthographic knowledge sources

Abstract

In embodiments, apparatuses, methods and storage media are described that are associated with recognition of speech based on sequences of named entities. Language models may be trained as being associated with sequences of named entities. A language model may be selected for speech recognition after identification of one or more sequences of named entities by an initial language model. After identification of the one or more sequences of named entities, weights may be assigned to the one or more sequences of named entities. These weights may be utilized to select a language module and/or update the initial language model to one that is associated with the identified one or more sequences of named entities. In various embodiments, the language model may be repeatedly updated until the recognized speech converges sufficiently to satisfy a predetermined threshold. Other embodiments may be described and claimed.

Claims

1. One or more computer-readable storage media comprising a plurality of instructions configured to cause one or more computing devices, in response to execution of the instructions by the computing device, to:

identify one or more sequences of parts of speech in a speech sample;

determine text spoken in the speech sample based at least in part on a language model associated with the one or more identified sequences.

2. The one or more computer-readable media of claim 1, wherein the parts of speech comprise named entities.

3. The computer-readable media of claim 2, wherein the instructions are further configured to cause the one or more computing devices to modify or replace the language model based at least in part on the sequences of named entities.

4. The computer-readable media of claim 3, wherein the instructions are further configured to cause the one or more computing devices to determine weights for the one or more sequences of named entities.

5. The computer-readable media of claim 4, wherein the instructions are further configured to cause the one or more computing devices to modify or replace the language model based at least in part on the weights for the one or more sequences of named entities.

6. The computer-readable media of claim 5, wherein the weights are sparse weights.

7. The computer-readable media of claim 5, wherein the instructions are further configured to cause the one or more computing devices to repeat the identify, determine weights, modify or replace, and determine text.

8. The computer-readable media of claim 7, wherein the instructions are further configured to cause the one or more computing devices to repeat until a convergence threshold is reached.

9. The computer-readable media of any of claim 2, wherein the instructions are further configured to cause the one or more computing devices to identify sequences of named entities based on text identified by the language model.

10. The computer-readable media of claim 2, wherein the instructions are further configured to cause the one or more computing devices to:

determine one or more phonemes from the speech; and

determine text from the one or more phonemes based at least in part on the language model.

11. The computer-readable media of claim 2, wherein the language model was trained based on one or more sequences of named entities associated with the language model.

12. The computer-readable media of claim 11, wherein the language model comprises a language model that was trained based on a sample of text that included the one or more sequences of named entities associated with the language model.

13. The computer-readable media of claim 2, wherein the instructions are further configured to cause the one or more computing devices to receive the speech sample.

14. One or more computer-readable storage media comprising a plurality of instructions configured to cause one or more computing devices, in response to execution of the instructions by the computing device, to:

identify one or more sequences of named entities in a text sample;

train a language model associated with the one or more sequences of named entities based on in part on the text sample.

15. The computer-readable media of claim 14, wherein the instructions are further configured to cause the computing device to:

identify one or more named entities in the text sample;

cluster sequences of named entities; and

associate a language module with the clustered sequences of named entities.

16. The computer-readable media of claim 14, wherein the instructions are further configured to cause the computing device to store the associated language model for subsequent speech recognition.

17. The computer-readable media of claim 14, wherein the language model is associated with a single cluster of named entity sequences.

18. The computer-readable media of claim 14, wherein the language model is associated with a small number of sequences of named entities.

19. An apparatus, comprising:

one or more computer processors; and

one or more modules configured to execute on the one or more computer processors to;

identify one or more sequences of named entities in a speech sample;

determine text spoken in the speech sample based at least in part on a language model associated with the one or more identified sequences.

20. The apparatus of claim 19, wherein the one or more modules are further configured to modify or replace the language model based at least in part on the sequences of named entities.

21. The apparatus of claim 20, wherein the one or more modules are further configured to:

determine weights for the one or more sequences of named entities; and

modify or replace the language model based at least in part on the weights for the one or more sequences of named entities.

22. The apparatus of claim 19, wherein the one or more modules are further configured to identify sequences of named entities based on text identified by the language model.

23. A computer-implemented method, comprising:

identifying, by a computing device, one or more sequences of named entities in a speech sample;

determining, by the computing device, text spoken in the speech sample based at least in part on a language model associated with the one or more identified sequences.

24. The method of claim 23, further comprising modifying or replacing, by the computing device, the language model based at least in part on the sequences of named entities.

25. The method of claim 23, further comprising identifying, by the computing device, sequences of named entities based on text identified by the language model.