Language model customization in speech recognition for speech analytics

1. A method for performing voice analytics on interactions with an organization, comprising:

training a customized language model for the organization by:

receiving, by a speech recognition engine, organization-specific training data and generic training data;

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

assigning, by the speech recognition engine, a plurality of weights to the generic training data through partitioning the generic training data into a plurality of partitions in accordance with the computed similarities wherein the computed similarities comprise a fixed set of one or more threshold 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;

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

training, by the speech recognition engine, the customized language model using the customized training data; and

outputting, by the speech recognition engine, the customized language model, the customized language model being configured to compute a likelihood of phrases in a medium;

receiving, by the speech recognition engine, an input speech from an interaction between a customer and an agent of the organization; and

performing voice analytics on the received input speech.

2. The method of claim 1 , wherein a silhouette score is used to determine a number of the plurality of partitions.

3. The method of claim 1 , wherein a test set of the generic training data and the organization-specific training data empirically determine a number of the plurality of partitions.

4. The method of claim 1 , wherein k-means clustering is used to determine a number of the plurality of partitions.

5. The method of claim 1 , wherein the desired weight of a partition is exponentially decreasing with decreasing partition similarity.

6. The method of claim 1 , wherein the training a customized language model for the organization further comprise:

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.

7. The method of claim 1 , wherein the organization-specific training data comprise at least one of: in-medium data and out-of-medium data.

8. The method of claim 7 , wherein the in-medium data comprise speech recognition transcript text and the out-of-medium data comprise non-speech text.

9. A voice analytics system comprising:

a speech model training system comprising:

a processor; and

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

receive organization-specific training data and generic training data;

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 through partitioning the generic training data into a plurality of partitions in accordance with the computed similarities wherein the computed similarities comprise a fixed set of one or more threshold 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;

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 of phrases in a medium; and

a speech analytics system configured to:

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

perform voice analytics on the received input speech.

10. The speech recognition system of claim 9 , wherein a silhouette score is used to determine a number of the plurality of partitions.

11. The speech recognition system of claim 9 , wherein a test set of the generic training data and the organization-specific training data empirically determine a number of the plurality of partitions.

12. The speech recognition system of claim 9 , wherein k-means clustering is used to determine a number of the plurality of partitions.

13. The speech recognition system of claim 9 , wherein the desired weight of a partition is exponentially decreasing with decreasing partition similarity.

14. The speech recognition system of claim 9 , wherein the memory of the speech training model system 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.

15. The speech recognition system of claim 9 , wherein the organization-specific training data comprise at least one of: in-medium data and out-of-medium data.

16. The speech recognition system of claim 15 , wherein the in-medium data comprise speech recognition transcript text and the out-of-medium data comprise non-speech text.