Laughter signature: a novel biometric trait for person identification

Folorunso, Comfort Oluwaseyi; Asaolu, Olumuyiwa; Popoola, Oluwatoyin P.

doi:10.1504/ijbm.2020.108480

Cited by 2 publications

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“…Therefore, incorporating laughter detection in automatic speech recognition systems can reduce word error rate by recognizing non-speech sounds, thereby improving the system. 1 This work proposes the use of the audio laughter only, and excludes the visual cues for gender recognition (GR) because laughter can be heard from a distance or in the dark, and be perceived or recorded without imagery. Gender is a multifaceted concept, which plays an imperative role as a social construct and essential form of an individual's personality.…”

Section: Introductionmentioning

confidence: 99%

“…The automatic detection of laughter occurrences in human speech can enhance automatic speaker recognition (ASR) systems, automatic speech recognition system, identify humorous content in video clips, and detect speaker's emotion. Therefore, incorporating laughter detection in automatic speech recognition systems can reduce word error rate by recognizing non‐speech sounds, thereby improving the system 1 . This work proposes the use of the audio laughter only, and excludes the visual cues for gender recognition (GR) because laughter can be heard from a distance or in the dark, and be perceived or recorded without imagery.…”

Section: Introductionmentioning

confidence: 99%

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Laughter signature, a new approach to gender recognition

Folorunso

Popoola

Asaolu

2020

Engineering Reports

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Gender Recognition (GR) is the process of identifying gender difference by extracting and evaluating features from data existing as images, video, audio, text, and other signals. The task of GR has been achieved using face, keystroke, gait, and speech features. "Laughter" with its intrinsic usefulness for this task is yet to be explored. Laughter is an important paralinguistic feature in human communication. It can change the meaning of speech when triggered by some form of arousal or amusement. Although, laughter can be "acted," but the human natural laughter is a spontaneous reflex response, which reasonably embeds some characteristic peculiarities of the individuals. The human brain has capacity to make this distinction. In this study, spontaneous laughter bouts of 123 volunteers (41 females and 82 males) were recorded. The Dynamic-Average of the Mel frequency Cepstral Coefficient (DA-MFCC) were generated and trained using two conventional and effective machine learning algorithms that have been employed in gender identification. These algorithms are Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) classifiers. Overall accuracies of 87.65% and 86.91% were obtained with GMM and SVM respectively. Therefore, indicating the possibility of using laughter characteristics as signatures for distinguishing between male and female genders. For both classifiers, the use of DA-MFCC reasonably reduced training time. Some of the potential areas of applications of GR include security, health care, marketing, human machine interaction toward enhanced emotion recognition, automatic speaker recognition and forensics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%