Emotive alert

Abstract

Voicemail has become an integral part of our personal and professional communication. The number of messages that accumulate in our voice mailboxes necessitate new ways of prioritizing them. Currently, we are forced to actively listen to all messages in order to find out which ones are important and which ones can be attended to later on. In this paper, we describe Emotive Alert, a system that can detect some of the significant emotions in a new message and notify the account owner along various affective axes, including urgency, formality, valence (happy vs. sad) and arousal (calm vs. excited). We have used a purely acoustic, HMM-based approach for identifying the emotions, which allows application of this system to all messages independent of language.

Emotion recognition using IG-based feature compensation and continuous support vector machines

“…In the past years, many researchers have paid their attention to emotion recognition via speech signals. Several important recognition models have been applied to the emotion recognition task, such as Neural Network (NN) [2], Hidden Markov Model (HMM) [3], and Support Vector Machine (SVM) [4] [5]. Besides the generally used prosodic and acoustic features, some special features are also applied for this task, such as TEO-based features [6].…”

Emotion recognition from audio

“…Son yıllarda, bu sistemlere, sayısallaştırılan ses bilgisinden yararlanarak kullanıcı duygu ve isteklerini analiz edebilen modüller eklenmesi yönünde çalışmalar yapılmaktadır [1,2].…”

“…Yapılan bir diğer çalışmada geliştirilen sistem "Emotive Alert" olarak adlandırılmakta ve tele-sekreterdeki konuşmaları duygu durumuna göre sınıflandırarak daha acil olanların seçilebilmesine olanak sağlamaktadır [2]. Türkçe üzerine bildiğimiz yapılan tek çalışma olan "Türkçe'de Duygu Çözümlemesi"nde, dil bilimciler tarafından temel frekansın Türkçe duygu çözümlemesindeki etkileri gözlemlenmiştir [3].…”