Machine Learning Based Emotion Recognition using Speech Signal

doi:10.35940/ijeat.a1068.1291s52019

Cited by 3 publications

(1 citation statement)

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“…They demonstrated the value of computerized acoustic analysis in recognizing clients’ productive expression of emotional experiences. Recent advances in machine learning permit researchers to analyze vocal affect expression automatically ( Vogt et al, 2008 ; Rabiei and Gasparetto, 2015 ; Kumar and Iqbal, 2019 ). Computerized speech recognition allows the tracking of emotion in speech in more natural settings.…”

Section: Introductionmentioning

confidence: 99%

Acoustic analysis of clients’ expression of self-compassion, self-criticism, and self-protection within emotion focused therapy video sessions

Bailey,

Halamová,

Vráblová

2024

Front. Psychol.

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IntroductionWhen it comes to the non-verbal communication of emotions, it is apparent that the human voice is one of the main ways of expressing emotion and is increasingly important in psychotherapeutic dialog. There is ample research focusing on the vocal expression of emotions. However, to date the analysis of the vocal quality of clients’ in-sessional emotional experience remains largely unexplored. Moreover, there is generally a gap within the psychotherapy literature in the understanding of the vocal character of self-compassion, self-criticism, and protective anger.MethodsIn this study we investigated how clients vocally convey self-compassion, self-protection and self-criticism in Emotion Focused therapy sessions. For this purpose we investigated 12 commercially available Emotion Focused Therapy videos that employed a two chair or empty chair dialog. Praat software was used for the acoustic analysis of the most common features – pitch (known as fundamental frequency or F0) and intensity (voice amplitude, i.e., loudness).ResultsResults showed that intensity was significantly higher for self-criticism and self-protection than for self-compassion. Regarding pitch the findings showed no significant differences between the three states.DiscussionMore research analyzing acoustic features in a larger number of cases is required to obtain a deeper understanding of clients’ vocal expression of self-compassion, self-protection and self-criticism in Emotion Focused Therapy.

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Section: Introductionmentioning

confidence: 99%

Acoustic analysis of clients’ expression of self-compassion, self-criticism, and self-protection within emotion focused therapy video sessions

Bailey,

Halamová,

Vráblová

2024

Front. Psychol.

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Machine learning technique-based emotion classification using speech signals

Kumar

Iqbal

2023

Soft Comput

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An Urdu speech corpus for emotion recognition

Asghar

Sohaib

Iftikhar

et al. 2022

PeerJ Computer Science

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Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. Speech interfaces offer humans an informal and comfortable means to communicate with machines. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. In this work, we develop the first emotional speech database of the Urdu language. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms. The Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coefficient (LPC), energy, spectral flux, spectral centroid, spectral roll-off, and zero-crossing were used as speech descriptors. The classification tests were performed on the emotional speech corpus collected from 20 different subjects. To evaluate the quality of speech emotions, subjective listing tests were conducted. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. Removing the disgust emotion significantly improves the performance of the classifier to 76.5%.

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Machine Learning Based Emotion Recognition using Speech Signal

Cited by 3 publications

References 54 publications

Acoustic analysis of clients’ expression of self-compassion, self-criticism, and self-protection within emotion focused therapy video sessions

Acoustic analysis of clients’ expression of self-compassion, self-criticism, and self-protection within emotion focused therapy video sessions

Machine learning technique-based emotion classification using speech signals

An Urdu speech corpus for emotion recognition

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