Method and system for detecting sentiment by analyzing human speech

• US 9,812,154 B2
• Filed: 01/19/2016
• Issued: 11/07/2017
• Est. Priority Date: 01/19/2016
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Inventors: Prasad, Prathosh Aragulla, Tyagi, Vivek
• Primary Examiner(s): Baker, Charlotte M
• Application Number:
  US15/000,068
• Publication Number:
  US 20170206915A1
• Time in Patent Office: 658 Days
• Field of Search: 704205, 704223, 704232
• CPC Class Codes: G10L 25/63 for estimating an emotional state
• CPC Class Codes: G10L 25/93 Discriminating between voiced and unvoiced parts of speech signals G10L25/90 takes precedence

Abstract

A method and a system for detecting sentiment of a human based on an analysis of human speech are disclosed. In an embodiment, one or more time instances of glottal closure are determined from a speech signal of the human. A voice source signal based on the determined one or more time instances of glottal closure is generated. A set of relative harmonic strengths is determined based on one or more harmonic contours of the voice source signal. The RHS is indicative of a deviation of the one or more harmonics of the voice source signal from a fundamental frequency of the voice source signal. A set of feature vectors is determined based on the RHS. The set of feature vectors are utilizable to detect the sentiment of the human.

Claims

1. A method for detecting sentiment of a human based on an analysis of human speech, the method comprising;

determining, by one or more processors, one or more time instances of glottal closure from a speech signal of the human;

generating, by the one or more processors, a voice source signal based on the determined one or more time instances of glottal closure;

determining, by the one or more processor, a set of relative harmonic strengths based on one or more harmonic contours of the voice source signal, wherein a relative harmonic strength (RHS) is indicative of a deviation of one or more harmonics of the voice source signal from a fundamental frequency of the voice source signal; and

determining, by the one or more processors, a set of feature vectors based on the set of relative harmonic strengths, wherein the set of feature vectors is utilizable to detect the sentiment of the human.

2. The method of claim 1 further comprising sampling, by the one or more processors, the received speech signal to obtain one or more speech frames of a pre-defined time duration.

3. The method of claim 2 further comprising extracting, by the one or more processors, one or more voiced speech frames and one or more unvoiced speech frames from each of the one or more speech frames, wherein the one or more time instances of glottal closures are determined for the one or more voiced speech frames.

4. The method of claim 1 further comprising determining, by the one or more processors, a pitch-synchronous harmonic spectrum of the voice source signal.

5. The method of claim 4 further comprising determining, by the one or more processors, the one or more harmonic contours based on the one or more harmonics of the voice source signal.

6. The method of claim 5, wherein the set of relative harmonic strengths is determined based on a signal analysis or a statistical analysis of the one or more harmonic contours.

7. The method of claim 6 further comprising determining, by the one or more processors, a set of feature vectors based on the set of relative harmonic strengths.

8. The method of claim 1 further comprising determining, by the one or more processors, a set of pitch features, a set of intensity features, and a set of duration features based on a statistical analysis of the speech signal.

9. The method of claim 8 further comprising detecting, by the one or more processors, the sentiment of the human based on one or more of the set of feature vectors, the set of pitch features, the set of intensity features, and the set of duration features using one or more trained classifiers.

10. The method of claim 9, wherein the one or more trained classifiers may comprise one or more of a Support Vector Machine (SVM), a Logistic Regression, a fundamental frequency Bayesian Classifier, a Decision Tree Classifier, a Copula-based Classifier, a K-Nearest Neighbors (KNN) Classifier, a Random Forest (RF) Classifier, or a deep neural net (DNN) classifier.

11. A system for detecting sentiment of a human based on an analysis of human speech, the system comprising;

one or more processors configured to;

determine one or more time instances of glottal closure from a speech signal of the human;

generate a voice source signal based on the determined one or more time instances of glottal closure;

determine a set of relative harmonic strengths based on one or more harmonic contours of the voice source signal, wherein a relative harmonic strength (RHS) is indicative of a deviation of one or more harmonics of the voice source signal from a fundamental frequency of the voice source signal; and

determine a set of feature vectors based on the set of relative harmonic strengths, wherein the set of feature vectors is utilizable to detect the sentiment of the human.

12. The system of claim 11, wherein the one or more processors are further configured to sample a speech signal to obtain one or more speech frames of a pre-defined time duration.

13. The system of claim 12, wherein the one or more processors are further configured to extract one or more voiced speech frames and one or more unvoiced speech frames from each of the one or more speech frames, wherein the one or more time instances of glottal closures are determined for the one or more voiced speech frames.

14. The system of claim 11, wherein the one or more processors are further configured to determine a pitch-synchronous harmonic spectrum of the voice source signal.

15. The system of claim 14, wherein the one or more processors are further configured to determine the one or more harmonic contours based on the one or more harmonics of the voice source signal.

16. The system of claim 15, wherein the set of relative harmonic strengths is determined based on a signal analysis or a statistical analysis of the one or more harmonic contours.

17. The system of claim 15, wherein the one or more processors are further configured to determine a set of feature vectors based on the set of relative harmonic strengths.

18. The system of claim 11, wherein the one or more processors are further configured to determine a set of pitch features, a set of intensity features, and a set of duration features based on a statistical analysis of the speech signal.

19. The system of claim 18, wherein the one or more processors are further configured to detect sentiment of the human based on one or more of the set of feature vectors, the set of pitch features, the set of intensity features, and the set of duration features using one or more trained classifiers.

20. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions for causing a computer comprising one or more processors to perform steps comprising:

determining, by one or more processors, one or more time instances of glottal closure from a speech signal of a human;

generating, by the one or more processors, a voice source signal based on the determined one or more time instances of glottal closure;

determining, by the one or more processor, a relative harmonic strengths based on one or more harmonic contours of the voice source signal, wherein a relative harmonic strength (RHS) is indicative of a deviation of one or more harmonics of the voice source signal from a fundamental frequency of the voice source signal; and

determining, by the one or more processors, a set of features vectors based on the set of relative harmonic strengths, wherein the set of features vectors is utilizable to detect sentiment of the human.