MFCCFusionNet: MFCC temsilleri kullanılarak konuşma duygu tanıma için CNN–DNN tabanlı bir özellik birleştirme modeli

Öz

Konuşma duygu tanıma, insan–makine etkileşimini geliştirme ve duygu farkındalığına sahip akıllı sistemlerin geliştirilmesini destekleme potansiyeli nedeniyle önemli bir araştırma alanı hâline gelmiştir. Bununla birlikte, aynı akustik özellik temsili kullanılarak eğitilen farklı derin öğrenme mimarilerinin nasıl performans gösterdiğine ilişkin daha fazla karşılaştırmalı kanıta ihtiyaç duyulmaktadır. Bu çalışmada, RAVDESS veri seti kullanılarak deneysel bir karşılaştırma yapılmıştır. Mel-frekansı kepstral katsayıları (MFCC’ler) tek özellik çıkarım yöntemi olarak kullanılmış ve üç derin öğrenme mimarisi tasarlanarak değerlendirilmiştir: Evrişimli Sinir Ağı (CNN), Derin Sinir Ağı (DNN) ve MFCCFusionNet olarak adlandırılan hibrit özellik birleştirme modeli. CNN ve DNN modelleri sırasıyla %81 ve %76 sınıflandırma doğruluğu elde ederken, önerilen MFCC-Fuse modeli %83 test doğruluğu ile değerlendirilen modeller arasında en yüksek başarıyı göstermiştir. Bulgular, MFCC tabanlı temsillerin farklı derin öğrenme mimarileri tarafından etkili biçimde işlenebildiğini ve özellik düzeyinde birleştirmenin konuşma duygu tanıma performansında sınırlı fakat olumlu bir iyileşme sağlayabileceğini göstermektedir. Genel olarak çalışma, MFCC özelliklerinden öğrenilen CNN ve DNN tabanlı temsillerin birleştirilmesinin konuşma tabanlı duygu tanıma modellerinin ayırt edici kapasitesini artırabileceğine ilişkin ön kanıt sunmaktadır.

Anahtar Kelimeler

• Konuşma duygu tanıma
• MFCC
• derin öğrenme mimarileri
• özellik birleştirme
• CNN
• DNN

MFCCFusionnet: A CNN–DNN feature-fusion model for speech emotion recognition using MFCC representations

Öz

Speech emotion recognition has become an increasingly important research area because of its potential to enhance human–machine interaction and support the development of emotion-aware systems. However, further comparative evidence is still needed on how different deep learning architectures perform when they are trained using the same acoustic feature representation. In this study, an experimental comparison was conducted using the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset. Mel-frequency cepstral coefficients (MFCCs) were used as the sole feature extraction method, and three deep learning architectures were designed and evaluated: a Convolutional Neural Network (CNN), a Deep Neural Network (DNN), and a hybrid feature-fusion model named MFCCFusionNet. The CNN and DNN models achieved classification accuracies of 81% and 76%, respectively. The proposed MFCCFusionNet model combined the learned representations obtained from the CNN and DNN sub-models and achieved the highest accuracy among the evaluated models, with a test accuracy of 83%. The findings indicate that MFCC-based representations can be effectively processed by different deep learning architectures and that feature-level fusion may improve classification performance in speech-based emotion recognition. The study provides preliminary evidence that feature-level fusion of CNN- and DNN-based MFCC representations may improve classification performance in speech emotion recognition.

Anahtar Kelimeler

• Speech emotion recognition
• MFCC
• deep learning architectures

Kaynakça

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