Language model customization in speech recognition for speech analytics

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.