Polish Emotional Speech Recognition Based on the Committee of Classifiers

Abstract

This article presents the novel method for emotion recognition from polish speech. We compared two different databases: spontaneous and acted out speech. For the purpose of this research we gathered a set of audio samples with emotional information, which serve as input database. Multiple Classifier Systems were used for classification, with commonly used speech descriptors and different groups of perceptual coefficients as features extracted from audio samples.. Streszczenie. Niniejsza praca dotyczy rozpoznawania stanów emocjonalnych na podstawie głosu. W artykule porównaliśmy mowę spontaniczną z mową odegraną. Na potrzeby zrealizowanych badań zgromadzone zostały emocjonalne nagrania audio, stanowiące kompleksową bazę wejściową. Przedstawiamy nowatorski sposób klasyfikacji emocji wykorzystujący komitety klasyfikujące, stosując do opisu emocji powszechnie używane deskryptory sygnału mowy oraz percepcyjne współczynniki hybrydowe. (Rozpoznawanie emocji na podstawie sygnału mowy przy użyciu komitetów klasyfikujących)

Emotional Speech Recognition Based on the Committee of Classifiers

“…Therefore prior to classification, methods of balancing a numerous features vector, feature selection or extraction are studied to speed up the learning process and minimize the curse of dimensionality problem [ 29 , 30 ]. Emotion classification is generally performed using standard techniques such as SVM [ 31 , 32 , 33 ], various types of artificial neural networks (NN) [ 34 , 35 , 36 , 37 ], different types of the k-NN classifier [ 19 , 38 ] or using Hidden Markov Model (HMM) and its variations [ 39 ]. However, it is a complex task with many unresolved issues.…”

“…Consequently, paralinguistic cues relating to emotion have a huge effect on ultimate meaning of the message [ 18 ]. This paper refers to my previous research [ 19 ], where the novel method for emotional speech recognition based on committee of classifiers was presented. This method is based on a set of classifiers (nodes) whose individual predictions are combined to make the final decision.…”

Capturing emotions in voice: A comparative analysis of methodologies in psychology and digital signal processing

“…The initial trend of using artificially created emotions has been recently replaced by searching for opportunities to investigate natural and spontaneous emotional speech, for example the data collected from an emergency call center in Poznań (Demenko & Jastrzębska, 2012) or a corpus of real-life conversations retrieved from an emergency call center, tagged with emotions described by Gałka and colleagues (2015). Kamińska and Sapiński (2017) collected a corpus of emotional speech containing samples extracted from discussions in TV programs.…”