Konuşma Duygu Tanıma için Akustik Özelliklere Dayalı LSTM Tabanlı Bir Yaklaşım

Year 2022, , 54 - 67, 07.12.2022

Kenan Donuk , Davut Hanbay

https://doi.org/10.53070/bbd.1113379

Cited By: 2

Abstract

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.

Keywords

Derin öğrenme, konuşma duygu tanıma, LSTM, ravdess, tess

An LSTM-Based Approach with Acoustic Features for Speech Emotion Recognition

Abstract

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.

Keywords

Deep learning, speech emotion recognition, long short-term memory, ravdess, tess

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