The Impact of Pre-processing and Feature Selection Methods for Speech Emotion Recognition

Year 2019, , 99 - 112, 15.03.2019

Turgut Özseven

https://doi.org/10.24012/dumf.498727

Cited By: 1

Abstract

Speech emotion
recognition uses features obtained from digital signal processing and digitized
sound signal.  All of the features
extracted from the speech can be handled under one dimension as well as grouped
in terms of dimensional or structure.  In
this study, the effects of feature selection and preprocessing methods on
emotion detection were investigated.  For
this purpose, EMO-DB data set and three different classifiers are used.  According to the results obtained, the
highest success was achieved with 90.3% with multi-layer perceptron and
high-pass filter.  Spectral features
provide higher success than prosodic features. 
In addition, females compared to males and individuals in 20-29 age
interval compared to individuals in 30-35 age interval reflect their emotions
more to their voices.  Among the
filtering methods obtained in the study, high-pass filtering increased the
success of classifier whereas low-pass filtering, band-pass filtering and noise
reduction reduced it.

Keywords

Speech emotion recognition, Pre-processing, Feature selection, Filtering, Emotion classification

Konuşma Tabanlı Duygu Tanımada Ön İşleme ve Öznitelik Seçim Yöntemlerinin Etkisi

Abstract

Konuşma tabanlı duygu tanımada sayısal sinyal işleme ile sayısal hale getirilen ses sinyalinden elde edilen öznitelikler kullanılmaktadır. Konuşmadan çıkartılan özniteliklerin tamamı tek boyut altında ele alınabildiği gibi boyutsal veya yapı bakımdan gruplandırılarak da ele alınmaktadır. Bu çalışmada, öznitelik seçim ve ön işleme yöntemlerinin duygu tanımadaki etkisi araştırılmıştır. Bu amaçla, EMO-DB veri seti ve üç farklı sınıflandırıcı kullanılmıştır. Elde edilen sonuçlara göre, en yüksek başarı çok katmanlı algılayıcı ve yüksek geçiren filtre ile %90.3 olarak elde edilmiştir. Spektral öznitelikler prosodik özniteliklerden daha yüksek başarı sağlamıştır. Ayrıca, bayanlar erkeklere göre ve 20-29 yaş aralığındaki bireyler 30-35 yaş aralığındaki bireylere göre duygularını seslerine daha fazla yansıtmaktadır. Çalışmada ele alınan ön işleme yöntemlerinden yüksek geçiren filtreler sınıflandırıcı başarısı artırırken alçak geçiren, bant geçiren filtreler ve gürültü giderme başarı oranını düşürmüştür.

Keywords

Konuşmadan duygu tanıma, ön işleme, öznitelik seçimi, filtreleme, duygu sınıflandırma

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