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Emotional Speech Recognition Using Deep Neural Networks
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In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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Abstract:
International audience ; The expression of emotions in human communication plays a very important role in the information that needs to be conveyed to the partner. The forms of expression of human emotions are very rich. It could be body language, facial expressions, eye contact, laughter, and tone of voice. The languages of the world’s peoples are different, but even without understanding a language in communication, people can almost understand part of the message that the other partner wants to convey with emotional expressions as mentioned. Among the forms of human emotional expression, the expression of emotions through voice is perhaps the most studied. This article presents our research on speech emotion recognition using deep neural networks such as CNN, CRNN, and GRU. We used the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus for the study with four emotions: anger, happiness, sadness, and neutrality. The feature parameters used for recognition include the Mel spectral coefficients and other parameters related to the spectrum and the intensity of the speech signal. The data augmentation was used by changing the voice and adding white noise. The results show that the GRU model gave the highest average recognition accuracy of 97.47%. This result is superior to existing studies on speech emotion recognition with the IEMOCAP corpus.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; CNN; CRNN; data augmentation; emotion; GRU; IEMOCAP; recognition; speech
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URL: https://hal.archives-ouvertes.fr/hal-03632853/document
https://hal.archives-ouvertes.fr/hal-03632853
https://doi.org/10.3390/s22041414
https://hal.archives-ouvertes.fr/hal-03632853/file/sensors-22-01414-v2.pdf
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| 2 |
Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
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In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
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| 3 |
Text Data Augmentation for the Korean Language
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3425 (2022)
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| 4 |
Emotional Speech Recognition Using Deep Neural Networks
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In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
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| 5 |
A Study of Data Augmentation for ASR Robustness in Low Bit Rate Contact Center Recordings Including Packet Losses
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1580 (2022)
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| 6 |
Modeling the effect of military oxygen masks on speech characteristics
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In: Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03325087 ; Interspeech 2021, Aug 2021, Brno, Czech Republic (2021)
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| 7 |
Simulating reading mistakes for child speech Transformer-based phone recognition
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In: Annual Conference of the International Speech Communication Association (INTERSPEECH) ; https://hal.archives-ouvertes.fr/hal-03257870 ; Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2021, Brno, Czech Republic (2021)
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| 8 |
A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild ...
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| 9 |
Effekten av textaugmenteringsstrategier på träffsäkerhet, F1-värde och viktat F1-värde ; The effect of text data augmentation strategies on Accuracy, F1-score, and weighted F1-score
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| 10 |
Using Data Augmentation and Time-Scale Modification to Improve ASR of Children’s Speech in Noisy Environments
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In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
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| 11 |
Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm
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In: Mathematics; Volume 10; Issue 1; Pages: 4 (2021)
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| 12 |
Volumetric changes at implant sites: A systematic appraisal of traditional methods and optical scanning- based digital technologies
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| 13 |
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
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| 14 |
Improving Short Text Classification Through Global Augmentation Methods
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In: Lecture Notes in Computer Science ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE) ; https://hal.inria.fr/hal-03414750 ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.385-399, ⟨10.1007/978-3-030-57321-8_21⟩ (2020)
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| 15 |
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
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In: SLT 2020 - IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070321 ; SLT 2020 - IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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| 16 |
Characterization and classification of semantic image-text relations ...
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| 17 |
Characterization and classification of semantic image-text relations ...
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| 18 |
Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems
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In: Sensors ; Volume 20 ; Issue 9 (2020)
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| 19 |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
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In: Fraunhofer IAIS (2020)
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| 20 |
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
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In: Proceedings of the Society for Computation in Linguistics (2020)
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