Automatic emotion recognition in speech is a research area with a wide range of applications in human interactions. The basic mathematical tool used for emotion recognition is Pattern recognition which involves three operations, namely, pre-processing, feature extraction and classification. This paper introduces a procedure for emotion recognition using Hidden Markov Models (HMM), which is used...

Children with Autism Spectrum Disorders (ASD) present significant difficulties to understand and express emotions. Systems have thus been proposed to provide objective measurements of acoustic features used by children suffering from ASD to encode emotion in speech. However, only a few studies have exploited such systems to compare different groups of children in their ability to express emotio...

Automatic speech emotion recognition is a process of recognizing emotions in speech. This has wide applications in the area of phsycatrics help and in robotics’he human computer interaction the challenging area of research. Any effective HCI system has two sections Training and testing. The techniques used in the system are feature extraction and classification. This paper focuses on the brief ...

This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected cla...

In this paper we demonstrate how the emotional state of the speaker in uences his or her speech. We show that recognition accuracy varies signi cantly depending on the emotional state of the speaker. Our system models the pronunciation variation of emotional speech both at the acoustic and prosodic level. We show that using emotion-speci c acoustic and prosodic models allows the system to discr...

The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models bas...

In this contribution we introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared throughout the paper. Within the first method a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased te...

Speech emotion recognition is a process where a speech file is recognized against the stored speech data set . It analyzes the data set according to the classifier and predicts results accordingly . In this scenario , a predicted output is one which matches the most with the data base. Several kinds of classifier have been used in this scenario . This paper represents different sections of the ...

Research in speech emotion recognition often involves features that are extracted in lab settings or scenarios where speech quality is high. However, a great deal of communication occurs through speech codecs, which alters the speech signal and features extracted from it. The purpose of this study is to report on the performance degradation in emotion recognition systems when speech is passed t...

In the present work we aim at performance optimization of a speaker-independent emotion recognition system through speech feature selection process. Specifically, relying on the speech feature set defined in the Interspeech 2009 Emotion Challenge, we studied the relative importance of the individual speech parameters, and based on their ranking, a subset of speech parameters that offered advant...