Emotion recognition from the human voice

Parlak, Cevahir; Di̇ri̇, Banu

doi:10.1109/siu.2013.6531196

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…MFCCs, extracted from spoken utterances, employ frame division with overlap to ensure a seamless transition and minimize data loss. The choice of frame length and scroll time is pivotal for emotion recognition, and their impact is evaluated using SVM and k-NNs algorithm on the EmoSTAR dataset [92]. The authors pointed out that employing a 10-fold cross-validation analysis yielded a noteworthy 98.7% accuracy in classifying emotions.…”

Section: A Personal Attributes Inferencementioning

confidence: 99%

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

Pudasaini,

Al-Hawawreh,

Bouadjenek

et al. 2024

Preprint

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Audio profiling is at the forefront of a technological breakthrough, offering rich insights into human behavior, emotions, physical attributes, and environmental contexts through detailed analysis of voice data. As we embrace an era where the integration of smart technologies equipped with the ability to capture sound is becoming omnipresent, the capacity to accurately infer personal traits such as age, gender, height, weight, emotional state, personality, and even environmental contexts through voice analysis opens up vast opportunities across law enforcement, healthcare, social and commercial services, and entertainment. This emerging field promises to enhance our interaction with technology by not only understanding who we are but also by interpreting the world around us. However, the exciting landscape is fraught with challenges, including data imbalances, the complexity of predictive models, and significant privacy concerns regarding handling sensitive paralinguistic information. This survey explores deep into the current landscape of audio profiling, examining the techniques and datasets in use, and showcasing its diverse applications while highlighting the need for advanced methodologies, enriched dataset development, and robust privacy preservation techniques.

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Section: A Personal Attributes Inferencementioning

confidence: 99%

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

Pudasaini,

Al-Hawawreh,

Bouadjenek

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Researchers have done studies about emotion recognition from human expression. They found that emotion changing will have a significant impact on individual voice features [4]. Therefore, emotional states and human sentimentality can be explored by investigating these features.…”

Section: Related Workmentioning

confidence: 99%

Proposing A Hybrid Approach for Emotion Classification using Audio and Video Data

Rafeh¹,

Khojasteh²,

Alobaidi³

2019

5th International Conference on Computer Science and Information Technology (CSTY 2019)

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Emotion recognition has been a research topic in the field of Human Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%.

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Emotion Recognition in Women’s Voices and Its Application to Improve Mood Through Light Color Control

Zarazua,

Juarez,

Sanchez

2024

Communications in Computer and Information Science

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Emotion recognition from the human voice

Cited by 7 publications

References 8 publications

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

Proposing A Hybrid Approach for Emotion Classification using Audio and Video Data

Emotion Recognition in Women’s Voices and Its Application to Improve Mood Through Light Color Control

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