Language model customization in speech recognition for speech analytics

• US 10,186,255 B2
• Filed: 08/25/2016
• Issued: 01/22/2019
• Est. Priority Date: 01/16/2016
• Status: Active Grant

• Current Assignee: Genesys Cloud Services Incorporated
• Original Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Inventors: Tapuhi, Tamir, Lev-Tov, Amir, Faizakof, Avraham, Konig, Yochai
• Primary Examiner(s): Rudolph, Vincent
• Assistant Examiner(s): Brinich, Stephen
• Application Number:
  US15/247,645
• Publication Number:
  US 20170206890A1
• Time in Patent Office: 880 Days
• Field of Search: 704 1- 10, 704244, 704256-2568, 704250-252, 704255, 704266
• CPC Class Codes: G06F 40/232 Orthographic correction, e.g. spell checking or vowelisation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/006 based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 2015/0635 updating or merging of old and new templates; Mean values; Weighting
• CPC Class Codes: G10L 2015/0636 Threshold criteria for the updating
• CPC Class Codes: G10L 2015/088 Word spotting

Abstract

A method for generating a language model for an organization includes: receiving, by a processor, organization-specific training data; receiving, by the processor, generic training data; computing, by the processor, a plurality of similarities between the generic training data and the organization-specific training data; assigning, by the processor, a plurality of weights to the generic training data in accordance with the computed similarities; combining, by the processor, the generic training data with the organization-specific training data in accordance with the weights to generate customized training data; training, by the processor, a customized language model using the customized training data; and outputting, by the processor, the customized language model, the customized language model being configured to compute the likelihood of phrases in a medium.

Claims

1. A method for performing speech recognition of interactions with an organization, comprising:

training a customized language model for the organization by;

receiving, by a processor, organization-specific training data comprising a plurality of organization-specific phrases;

receiving, by the processor, generic training data comprising a plurality of generic phrases;

computing, by the processor, a plurality of similarities between the generic training data and the organization-specific training data;

assigning, by the processor, a plurality of weights to the generic training data in accordance with the computed similarities;

combining, by the processor, the generic training data with the organization-specific training data in accordance with the weights to generate customized training data;

training, by the processor, the customized language model using the customized training data; and

outputting, by the processor, the customized language model, the customized language model being configured to compute the likelihood that an input phrase will appear in a communication medium in an interaction with the organization; and

receiving input speech from an interaction between a customer and a contact center of the organization;

transcribing the received input speech, by an automatic speech recognition engine configured with the customized language model, to generate a transcript of the input speech; and

performing voice analytics on the transcript of the input speech.

2. The method of claim 1, wherein the organization-specific training data comprise in-medium data and out-of-medium data.

3. The method of claim 2, wherein the in-medium data are speech recognition transcript text and the out-of-medium data are non-speech text.

4. The method of claim 1, wherein the organization-specific training data does not include in-medium data.

5. The method of claim 1, wherein the assigning the plurality of weights to the generic training data comprises:

partitioning the generic training data into a plurality of partitions in accordance with the computed similarities;

associating a partition similarity with each of the partitions, the partition similarity corresponding to the average similarity of the data in the partition; and

assigning a desired weight to each partition, the desired weight corresponding to the partition similarity of the partition.

6. The method of claim 5, wherein the desired weight of a partition is exponentially decreasing with decreasing partition similarity.

7. The method of claim 1, further comprising:

receiving organization-specific in-medium data;

combining the organization-specific in-medium data with the generic training data and the organization-specific training data to generate the customized training data; and

retraining the language model in accordance with the customized training data.

8. A system configured to perform speech recognition of interactions with an organization, the system comprising:

a processor;

memory coupled to the processor and storing instructions that, when executed by the processor, cause the processor to;

receive organization-specific training data comprising a plurality of organization-specific phrases;

receive generic training data comprising a plurality of generic phrases;

compute a plurality of similarities between the generic training data and the organization-specific training data;

assign a plurality of weights to the generic training data in accordance with the computed similarities;

combine the generic training data with the organization-specific training data in accordance with the weights to generate customized training data;

train a customized language model using the customized training data; and

output the customized language model, the customized language model being configured to compute the likelihood that an input phrase will appear in a communication medium in an interaction with the organization; and

a speech recognition module configured to;

receive input speech from an interaction between a customer and a contact center of the organization;

transcribe the received input speech, by an automatic speech recognition engine configured with the customized language model, to generate a transcript of the input speech; and

perform voice analytics on the transcript of the input speech.

9. The system of claim 8, wherein the organization-specific training data comprise in-medium data and out-of-medium data.

10. The system of claim 9, wherein the in-medium data are speech recognition transcript text and the out-of-medium data are non-speech text.

11. The system of claim 8, wherein the organization-specific training data does not include in-medium data.

12. The system of claim 8, wherein the memory further stores instructions that, when executed by the processor, cause the processor to assign the plurality of weights to the generic training data by:

partitioning the generic training data into a plurality of partitions in accordance with the computed similarities;

associating a partition similarity with each of the partitions, the partition similarity corresponding to the average similarity of the data in the partition; and

assigning a desired weight to each partition, the desired weight corresponding to the partition similarity of the partition.

13. The system of claim 12, wherein the desired weight of a partition is exponentially decreasing with decreasing partition similarity.

14. The system of claim 8, wherein the memory further stores instructions that, when executed by the processor, cause the processor to:

receive organization-specific in-medium data;

combine the organization-specific in-medium data with the generic training data and the organization-specific training data to generate the customized training data; and

retrain the language model in accordance with the customized training data.