Speech Emotion Recognition: Humans vs Machines
Introduction. The study focuses on emotional speech perception and speech emotion recognition using prosodic clues alone. Theoretical problems of defining prosody, intonation and emotion along with the challenges of emotion classification are discussed. An overview of acoustic and perceptional corre...
| Κύριοι συγγραφείς: | , |
|---|---|
| Μορφή: | Άρθρο |
| Γλώσσα: | English |
| Έκδοση: |
Saint Petersburg Electrotechnical University
2019-12-01
|
| Σειρά: | Дискурс |
| Θέματα: | |
| Διαθέσιμο Online: | https://discourse.elpub.ru/jour/article/view/289 |
| _version_ | 1826569555369000960 |
|---|---|
| author | S. Werner G. N. Petrenko |
| author_facet | S. Werner G. N. Petrenko |
| author_sort | S. Werner |
| collection | DOAJ |
| description | Introduction. The study focuses on emotional speech perception and speech emotion recognition using prosodic clues alone. Theoretical problems of defining prosody, intonation and emotion along with the challenges of emotion classification are discussed. An overview of acoustic and perceptional correlates of emotions found in speech is provided. Technical approaches to speech emotion recognition are also considered in the light of the latest emotional speech automatic classification experiments.Methodology and sources. The typical “big six” classification commonly used in technical applications is chosen and modified to include such emotions as disgust and shame. A database of emotional speech in Russian is created under sound laboratory conditions. A perception experiment is run using Praat software’s experimental environment.Results and discussion. Cross-cultural emotion recognition possibilities are revealed, as the Finnish and international participants recognised about a half of samples correctly. Nonetheless, native speakers of Russian appear to distinguish a larger proportion of emotions correctly. The effects of foreign languages knowledge, musical training and gender on the performance in the experiment were insufficiently prominent. The most commonly confused pairs of emotions, such as shame and sadness, surprise and fear, anger and disgust as well as confusions with neutral emotion were also given due attention.Conclusion. The work can contribute to psychological studies, clarifying emotion classification and gender aspect of emotionality, linguistic research, providing new evidence for prosodic and comparative language studies, and language technology, deepening the understanding of possible challenges for SER systems. |
| first_indexed | 2024-04-10T03:23:03Z |
| format | Article |
| id | doaj.art-40e2b5fa452d4a65aeb76b8fecf855f8 |
| institution | Directory Open Access Journal |
| issn | 2412-8562 2658-7777 |
| language | English |
| last_indexed | 2025-03-14T11:41:12Z |
| publishDate | 2019-12-01 |
| publisher | Saint Petersburg Electrotechnical University |
| record_format | Article |
| series | Дискурс |
| spelling | doaj.art-40e2b5fa452d4a65aeb76b8fecf855f82025-03-02T09:57:01ZengSaint Petersburg Electrotechnical UniversityДискурс2412-85622658-77772019-12-015513615210.32603/2412-8562-2019-5-5-136-152287Speech Emotion Recognition: Humans vs MachinesS. Werner0G. N. Petrenko1University of Eastern FinlandSaint Petersburg Electrotechnical UniversityIntroduction. The study focuses on emotional speech perception and speech emotion recognition using prosodic clues alone. Theoretical problems of defining prosody, intonation and emotion along with the challenges of emotion classification are discussed. An overview of acoustic and perceptional correlates of emotions found in speech is provided. Technical approaches to speech emotion recognition are also considered in the light of the latest emotional speech automatic classification experiments.Methodology and sources. The typical “big six” classification commonly used in technical applications is chosen and modified to include such emotions as disgust and shame. A database of emotional speech in Russian is created under sound laboratory conditions. A perception experiment is run using Praat software’s experimental environment.Results and discussion. Cross-cultural emotion recognition possibilities are revealed, as the Finnish and international participants recognised about a half of samples correctly. Nonetheless, native speakers of Russian appear to distinguish a larger proportion of emotions correctly. The effects of foreign languages knowledge, musical training and gender on the performance in the experiment were insufficiently prominent. The most commonly confused pairs of emotions, such as shame and sadness, surprise and fear, anger and disgust as well as confusions with neutral emotion were also given due attention.Conclusion. The work can contribute to psychological studies, clarifying emotion classification and gender aspect of emotionality, linguistic research, providing new evidence for prosodic and comparative language studies, and language technology, deepening the understanding of possible challenges for SER systems.https://discourse.elpub.ru/jour/article/view/289emotional speechspeech emotion perceptionspeech emotion recognitionrussian emotional speech databaseemotional speech corporaemotion classification |
| spellingShingle | S. Werner G. N. Petrenko Speech Emotion Recognition: Humans vs Machines Дискурс emotional speech speech emotion perception speech emotion recognition russian emotional speech database emotional speech corpora emotion classification |
| title | Speech Emotion Recognition: Humans vs Machines |
| title_full | Speech Emotion Recognition: Humans vs Machines |
| title_fullStr | Speech Emotion Recognition: Humans vs Machines |
| title_full_unstemmed | Speech Emotion Recognition: Humans vs Machines |
| title_short | Speech Emotion Recognition: Humans vs Machines |
| title_sort | speech emotion recognition humans vs machines |
| topic | emotional speech speech emotion perception speech emotion recognition russian emotional speech database emotional speech corpora emotion classification |
| url | https://discourse.elpub.ru/jour/article/view/289 |
| work_keys_str_mv | AT swerner speechemotionrecognitionhumansvsmachines AT gnpetrenko speechemotionrecognitionhumansvsmachines |