System, method and apparatus for voice analytics of recorded audio

• US 8,781,880 B2
• Filed: 06/04/2013
• Issued: 07/15/2014
• Est. Priority Date: 06/05/2012
• Status: Active Grant

• Current Assignee: Rank Miner, Inc.
• Original Assignee: Rank Miner, Inc.
• Inventors: Kovacs, Kornel, Feher, Istvan, Kocsor, Andras, Bodogh, Attila
• Primary Examiner(s): Boswell, Beth V
• Assistant Examiner(s): KASSIM, HAFIZ A
• Application Number:
  US13/909,351
• Publication Number:
  US 20130325560A1
• Time in Patent Office: 406 Days
• Field of Search: 705/7.37
• US Class Current: 705/7.37
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: H04M 3/42221 Conversation recording systems at the subscriber's set H04M1/656
• CPC Class Codes: H04M 3/51 Centralised call answering arrangements requiring operator intervention , e.g. call or contact centers for telemarketing

Abstract

Disclosed is a method for analyzing recorded telephone calls in order to predict business outcomes. The method involves recording a series of initial telephone calls and analyzing those calls for particular audio features. The audio features are tabulated and annotated to specify whether the telephone call resulted in a pre-determined business outcome. A model is then built whereby the pre-determined business outcome can be predicted based upon the presence of certain audio features. The model can subsequently be used to predict whether future calls are likely to result in the pre-determined business outcome.

Claims

1. A method for predicting business outcomes based upon recorded telephone calls, the method comprising:

using a feature extraction engine for using a series of recorded telephone calls of individuals calling a business, processing the recorded telephone calls as sound files, extracting various audio features from each of the telephone calls, and building a feature matrix whereby a plurality of extracted audio features are tabulated, wherein each audio feature is a first dimension in the feature matrix and each telephone call is a second dimension in the feature matrix, wherein the first dimension being one of a row or a column and the second dimension being the other one of the row or the column, wherein extracting various audio features comprises extracting emotional information comprising changes in emotional states;

using a model builder engine for determining whether the telephone calls resulted in a pre-determined business outcome, annotating the feature matrix by indicating whether the pre-determined business outcome was achieved by adding to the first dimension of the feature matrix a business outcome, and establishing a model whereby the pre-determined business outcome is associated with a subset of the extracted audio features;

using a prediction engine for using additional telephone calls from individuals calling the business, processing the additional telephone calls as sound files, extracting various audio features from each of the sound files, predicting whether the pre-determined business outcome will occur based upon the established model, and ranking the additional telephone calls based upon the prediction;

using a refining engine for determining whether each additional telephone call resulted in the actual occurrence of the pre-determined business outcome, comparing the actual occurrence with the prediction and modifying the established model on the basis of the comparison; and

using a Lift measure, Lift_p(f_s), as an index of performance of the model wherein

Lift_p(f_s)=f_s(p)/f_r(p)

where f_s(p) is an accuracy of the a new model, S, in the case of a first p percent of elements and f_r(p) provides a percentage of the model'"'"'s performance.

2. The method as described in claim 1 wherein the extracted audio features are in the English language.

3. The method as described in claim 1 wherein the extracted audio feature is in a language other than English.

4. The method of claim 1, wherein extracting various audio features further comprises extracting features based on emotional changes in speakers and consecutive agent-client segments.

5. The method of claim 1, wherein extracting various audio features further comprises extracting features comprising speaker separation, human speech segmentation and communication and intonation features.

6. A method for predicting business outcomes comprising:

using a feature extraction engine for using a series of recorded telephone calls, processing the recorded telephone calls as sound files, and extracting various audio features from each of the telephone calls, wherein extracting various audio features comprises extracting emotional information comprising changes in emotional states;

using a model builder engine for determining whether the telephone calls resulted in a pre-determined business outcome, and establishing a model whereby the pre-determined business outcome is associated with a subset of the extracted audio features;

using a prediction engine for using additional telephone calls, processing the additional telephone calls as sound files, and extracting various audio features from each of the sound files, and predicting whether the pre-determined business outcome will occur based upon the established model; and

using a Lift measure, Lift_p(f_s), as an index of performance of the model, wherein

Lift_p)f_s)=f_s(p)/f_r(p)

where f_s(p) is an accuracy of the a new model, S, in the case of a first p percent of elements and f_r(p) provides a percentage of the model'"'"'s performance.

7. The method as described in claim 6, further comprising ranking the additional telephone calls based upon the prediction.

8. The method as described in claim 7 wherein the rankings determine how the additional telephone calls are handled.

9. The method as described in claim 6, further comprising using a refining engine for determining whether each additional telephone call resulted in the actual occurrence of the pre-determined business outcome, comparing the actual occurrence with the prediction and modifying the established model on the basis of the comparison.

10. The method as described in claim 6, further comprising:

building a feature matrix whereby a plurality of extracted audio features are tabulated, wherein each audio feature is a first dimension in the feature matrix and each telephone call is a second dimension in the feature matrix, wherein the first dimension being one of a row or a column and the second dimension being the other one of the row or the column; and

annotating the feature matrix by indicating whether the pre-determined business outcome was achieved for a particular telephone call by adding to the first dimension of the feature matrix a business outcome.

11. The method as described in claim 6 wherein the audio features further comprises voice volume, tone, emotion, and inflection.

12. The method as described in claim 6 wherein the pre-determined business outcome is whether or not a sale was completed as a result of the telephone call.

13. The method of claim 6, wherein extracting various audio features further comprises extracting features based on emotional changes in speakers and consecutive agent-client segments.

14. The method of claim 6, wherein extracting various audio features further comprises extracting features comprising speaker separation, human speech segmentation and communication and intonation features.

15. A method for predicting business outcomes comprising

using a feature extraction engine for processing a series of telephone calls, and extracting various audio features from each of the telephone calls, wherein extracting various audio features comprises extracting emotional information comprising changes in emotional states;

using a model builder engine for determining whether the telephone calls resulted in a favorable business outcome, and establishing a model whereby the favorable business outcome is associated with the extracted audio features;

using a prediction engine for processing an additional telephone call, extracting various audio features from the additional telephone call, and predicting whether the favorable business outcome will occur based upon the established model; and

using a Lift measure, Lift_p(f_s), as an index of performance of the model, wherein

Lift_p)f_s)=f_s(p)/f_r(p)

where f_s(p) is an accuracy of the a new model, S, in the case of a first p percent of elements and f_r(p) provides a percentage of the model'"'"'s performance.

16. The method of claim 15, wherein extracting various audio features further comprises extracting features based on emotional changes in speakers and consecutive agent-client segments.

17. The method of claim 15, wherein extracting various audio features further comprises extracting features comprising speaker separation, human speech segmentation and communication and intonation features.