Öz
Konuşma duygu tanıma, konuşma sinyallerinden insan duygularını gerçek zamanlı olarak tanıyabilen aktif bir insan-bilgisayar etkileşimi alanıdır. Bu alanda yapılan tanıma görevi, duyguların karmaşıklığı nedeniyle zorlu bir sınıflandırma örneğidir. Etkili bir sınıflandırma işleminin yapılabilmesi yüksek seviyeli derin özelliklere ve uygun bir derin öğrenme modeline bağlıdır. Konuşma duygu tanıma alanında yapılmış birçok sınıflandırma çalışması mevcuttur. Bu çalışmalarda konuşma verilerinden duyguların doğru bir şekilde çıkarılması için birçok farklı model ve özellik birleşimi önerilmiştir. Bu makalede konuşma duygu tanıma görevi için bir sistem önerilmektedir. Bu sistemde konuşma duygu tanıma için uzun-kısa süreli bellek tabanlı bir derin öğrenme modeli önerilmiştir. Önerilen sistem ön-işlem, özellik çıkarma, özellik birleşimi, uzun-kısa süreli bellek ve sınıflandırma olmak üzere dört aşamadan oluşmaktadır. Önerilen sistemde konuşma verilerine ilk olarak kırpma ve ön-vurgu ön-işlemleri uygulanır. Bu işlemlerden sonra elde edilen konuşma verilerinden Mel Frekans Kepstrum Katsayıları, Sıfır Geçiş Oranı ve Kök Ortalama Kare Enerji akustik özellikleri çıkarılarak birleştirilir. Birleştirilen bu özelliklerin uzamsal bilgilerinin yanında zaman içindeki akustik değişimleri sistemde önerilen uzun-kısa süreli bellek ve buna bağlı bir derin sinir ağı modeliyle öğrenilir. Son olarak softmax aktivasyon fonksiyonu ile öğrenilen bilgiler 8 farklı duyguya sınıflandırılır. Önerilen sistem RAVDESS ve TESS veri setlerinin birlikte kullanıldığı bir veri kümesinde test edilmiştir. Eğitim, doğrulama ve test sonuçlarında sırasıyla %99.87 , %85.14 , %88.92 oranlarında doğruluklar ölçülmüştür. Sonuçlar, son teknoloji çalışmalardaki doğruluklarla kıyaslanmış önerilen sistemin başarısı ortaya konmuştur.
Anahtar Kelimeler
Kaynakça
- Cai L, Dong J & Wei M. (2020) Multi-Modal Emotion Recognition from Speech and Facial Expression Based on Deep Learning. Proceedings - 2020 Chinese Automation Congress, CAC 2020, pp. 5726–5729.
- Issa D, Fatih Demirci M, Yazici A (2020) Speech emotion recognition with deep convolutional neural networks. Biomedical Signal Processing and Control 59:101894.
- Atila O, Şengür A (2021) Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition. Applied Acoustics 182:108260.
- Mujaddidurrahman A, Ernawan F, Wibowo A, Sarwoko E. A, Sugiharto A, Wahyudi M. D. R. (2021) Speech Emotion Recognition Using 2D-CNN with Data Augmentation. 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), pp. 685–689.
- Padi S, Manocha D, Sriram R. D (2020) Multi-Window Data Augmentation Approach for Speech Emotion Recognition. http://arxiv.org/abs/2010.09895
- Nasim A. S, Chowdory R. H, Dey A, Das A. (2021) Recognizing Speech Emotion Based on Acoustic Features Using Machine Learning. 2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021. https://doi.org/10.1109/ICACSIS53237.2021.9631319
- Asiya U. A, Kiran V. K. (2021) Speech Emotion Recognition-A Deep Learning Approach. Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2021, pp. 867–871.
- Öztürk Ö. F, Pashaei E (2021) Konuşmalardaki duygunun evrişimsel LSTM modeli ile tespiti. Convolutional LSTM model for speech emotion recognition. DUJE (Dicle University Journal of Engineering) 12:581–589.
- Hochreiter S, Schmidhuber J. (1997) Long Short-Term Memory. Neural Computation 9(8):1735–1780. https://doi.org/10.1162/NECO.1997.9.8.1735
- Livingstone S. R, Russo F. A (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE 13(5):e0196391. https://doi.org/10.1371/JOURNAL.PONE.0196391
- Zenodo (2022) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) | Zenodo. https://zenodo.org/record/1188976#.YiypnHpBy71. Accessed 12 March 2022.
- University of Toronto Dataverse (2022) Toronto emotional speech set (TESS). https://dataverse.scholarsportal.info/dataset.xhtml?persistentId=doi:10.5683/SP2/E8H2MF. Accessed 6 May 2022.
- Davis S. B, Mermelstein P (1980) Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing 28(4):357–366.
- Chen Q, Huang G (2021) A novel dual attention-based BLSTM with hybrid features in speech emotion recognition. Engineering Applications of Artificial Intelligence 102:104277.
- Ancilin J, Milton A (2021) Improved speech emotion recognition with Mel frequency magnitude coefficient. Applied Acoustics 179:108046.
- Sun J (2019) Research on vocal sounding based on spectrum image analysis. Eurasip Journal on Image and Video Processing 2019(1). https://doi.org/10.1186/S13640-018-0397-0
- Stevens S. S, Volkmann J, Newman E. B (1937) A Scale for the Measurement of the Psychological Magnitude Pitch. Journal of the Acoustical Society of America, 8(3):185–190.
- O’Shaughnessy D. (1987) Speech communication : human and machine. In Wikipedia. Addison-Wesley.
- Wikipedia (2022) Discrete Cosine Transform. https://en.wikipedia.org/wiki/Discrete_cosine_transform. Accessed 10 March 2022.
- Ahmed N, Natarajan T, Rao K. R (1974) Discrete Cosine Transform. IEEE Transactions on Computers C–23(1):90–93. https://doi.org/10.1109/T-C.1974.223784
- Silva A. C. M. da, Coelho M. A. N, Neto R. F (2020) A Music Classification model based on metric learning applied to MP3 audio files. Expert Systems with Applications, 144:113071.
- Giannakopoulos T, Pikrakis A. (2014) Introduction to Audio Analysis: A MATLAB Approach, pp. 1–266.
- Wikipedia (2022) Zero-crossing rate. https://en.wikipedia.org/wiki/Zero-crossing_rate. Accessed 26 April 2022.
- Alías F, Socoró J. C, Sevillano X (2016) A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds. Applied Sciences 6(5):143.
- Librosa (2022) librosa 0.9.1 documentation. https://librosa.org/doc/latest/index.html. Accessed 16 April 2022.
Öz
Speech emotion recognition is an area of active human-computer interaction that can recognize human emotions from speech signals in real time. The recognition task in this area is an example of a difficult classification due to the complexity of emotions. An effective classification process depends on high-level deep features and an appropriate deep learning model. There are many classification studies in the field of speech emotion recognition. In these studies, many different models and combinations of features have been proposed to accurately extract emotions from speech data. In this article, a system for speech emotion recognition task is proposed. In this system, a long-short-term memory-based deep learning model is proposed for speech emotion recognition. The proposed system consists of four stages: preprocessing, feature extraction, feature combination, long-short-term memory and classification. In the proposed system, the clipping and pre-emphasis pre-processes are applied to the speech data first. After these processes, Mel Frequency Kepstrum Coefficients, Zero Crossing Ratio and Root Mean Square Energy acoustic properties are extracted from the obtained speech data and combined. In addition to the spatial information of these combined features, their acoustic changes over time are learned with the proposed long-short-term memory and a deep neural network model associated with it. Finally, the information learned is classified into 8 different emotions by the softmax activation function. The proposed system has been tested on a dataset using RAVDESS and TESS datasets together. Accuracies of 99.87%, 85.14% and 88.92% were measured in training, validation and test results, respectively. The results were compared in terms of the accuracies in the recent studies and the success of the proposed system was revealed.
Anahtar Kelimeler
Deep learning speech emotion recognition long short-term memory ravdess tess
Kaynakça
- Cai L, Dong J & Wei M. (2020) Multi-Modal Emotion Recognition from Speech and Facial Expression Based on Deep Learning. Proceedings - 2020 Chinese Automation Congress, CAC 2020, pp. 5726–5729.
- Issa D, Fatih Demirci M, Yazici A (2020) Speech emotion recognition with deep convolutional neural networks. Biomedical Signal Processing and Control 59:101894.
- Atila O, Şengür A (2021) Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition. Applied Acoustics 182:108260.
- Mujaddidurrahman A, Ernawan F, Wibowo A, Sarwoko E. A, Sugiharto A, Wahyudi M. D. R. (2021) Speech Emotion Recognition Using 2D-CNN with Data Augmentation. 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), pp. 685–689.
- Padi S, Manocha D, Sriram R. D (2020) Multi-Window Data Augmentation Approach for Speech Emotion Recognition. http://arxiv.org/abs/2010.09895
- Nasim A. S, Chowdory R. H, Dey A, Das A. (2021) Recognizing Speech Emotion Based on Acoustic Features Using Machine Learning. 2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021. https://doi.org/10.1109/ICACSIS53237.2021.9631319
- Asiya U. A, Kiran V. K. (2021) Speech Emotion Recognition-A Deep Learning Approach. Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2021, pp. 867–871.
- Öztürk Ö. F, Pashaei E (2021) Konuşmalardaki duygunun evrişimsel LSTM modeli ile tespiti. Convolutional LSTM model for speech emotion recognition. DUJE (Dicle University Journal of Engineering) 12:581–589.
- Hochreiter S, Schmidhuber J. (1997) Long Short-Term Memory. Neural Computation 9(8):1735–1780. https://doi.org/10.1162/NECO.1997.9.8.1735
- Livingstone S. R, Russo F. A (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE 13(5):e0196391. https://doi.org/10.1371/JOURNAL.PONE.0196391
- Zenodo (2022) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) | Zenodo. https://zenodo.org/record/1188976#.YiypnHpBy71. Accessed 12 March 2022.
- University of Toronto Dataverse (2022) Toronto emotional speech set (TESS). https://dataverse.scholarsportal.info/dataset.xhtml?persistentId=doi:10.5683/SP2/E8H2MF. Accessed 6 May 2022.
- Davis S. B, Mermelstein P (1980) Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing 28(4):357–366.
- Chen Q, Huang G (2021) A novel dual attention-based BLSTM with hybrid features in speech emotion recognition. Engineering Applications of Artificial Intelligence 102:104277.
- Ancilin J, Milton A (2021) Improved speech emotion recognition with Mel frequency magnitude coefficient. Applied Acoustics 179:108046.
- Sun J (2019) Research on vocal sounding based on spectrum image analysis. Eurasip Journal on Image and Video Processing 2019(1). https://doi.org/10.1186/S13640-018-0397-0
- Stevens S. S, Volkmann J, Newman E. B (1937) A Scale for the Measurement of the Psychological Magnitude Pitch. Journal of the Acoustical Society of America, 8(3):185–190.
- O’Shaughnessy D. (1987) Speech communication : human and machine. In Wikipedia. Addison-Wesley.
- Wikipedia (2022) Discrete Cosine Transform. https://en.wikipedia.org/wiki/Discrete_cosine_transform. Accessed 10 March 2022.
- Ahmed N, Natarajan T, Rao K. R (1974) Discrete Cosine Transform. IEEE Transactions on Computers C–23(1):90–93. https://doi.org/10.1109/T-C.1974.223784
- Silva A. C. M. da, Coelho M. A. N, Neto R. F (2020) A Music Classification model based on metric learning applied to MP3 audio files. Expert Systems with Applications, 144:113071.
- Giannakopoulos T, Pikrakis A. (2014) Introduction to Audio Analysis: A MATLAB Approach, pp. 1–266.
- Wikipedia (2022) Zero-crossing rate. https://en.wikipedia.org/wiki/Zero-crossing_rate. Accessed 26 April 2022.
- Alías F, Socoró J. C, Sevillano X (2016) A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds. Applied Sciences 6(5):143.
- Librosa (2022) librosa 0.9.1 documentation. https://librosa.org/doc/latest/index.html. Accessed 16 April 2022.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Yapay Zeka |
| Bölüm | PAPERS |
| Yazarlar | |
| Yayımlanma Tarihi | 7 Aralık 2022 |
| Gönderilme Tarihi | 6 Mayıs 2022 |
| Kabul Tarihi | 21 Haziran 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: Vol:7 Sayı: Issue:2 |
Cited By
CREMA-D: Improving Accuracy with BPSO-Based Feature Selection for Emotion Recognition Using Speech
Journal of Soft Computing and Artificial Intelligence
https://doi.org/10.55195/jscai.1214312
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