To date, little research has been done in emotion classification and recognition in speech. Therefore, there is a need to discuss why this topic is interesting and present a system for classifying and recognizing emotions through speech using neural networks through this article. The proposed system will be speaker independent since a database of speech samples will be used. Various classifiers...

Understanding how speech recognition problems affect the interaction with the user is a topic of great interest for the spoken dialogue community. In this paper, we examine the dependencies between speech recognition problems in adjacent turns. We also examine the dependencies between speech recognition problems and student emotions within a turn and in adjacent turns. We apply Chi Square ( ) a...

This paper proposes an interactive approach to spoken interactive open-domain question answering (ODQA) systems. The goal of ODQA systems is to extract an exact answer to user’s question from unstructured information sources such as large text corpora. When the reliabilities for answer hypotheses obtained by an ODQA system are low, systems need more information to effectively distinguish the ex...

This paper presents an approach to feature compensation for emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are firstly extracted. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional states, a feature compensation approach i...

In this study, we propose a new method of adapting language models for speech recognition using para-linguistic and extralinguistic features in speech. When we talk with others, we often change the way of lexical choice and speaking style according to various contextual factors. This fact indicates that the performance of automatic speech recognition can be improved by taking the contextual fac...

Cochlear implant (CI) speech performance is typically evaluated using well-enunciated speech produced at a normal rate by a single talker. CI users often have greater difficulty with variations in speech production encountered in everyday listening. Within a single talker, speaking rate, amplitude, duration, and voice pitch information may be quite variable, depending on the production context....

During production of emotional speech there are deviations in the components of speech production mechanism when compared to normal speech. The objective of this study is to capture the deviations in features related to the excitation source component of speech, and to develop a system for automatic recognition of emotions based on these deviations. The emotions considered for this study are: a...

In early research the basic acoustic features were the primary choices for emotion recognition from speech. Most of the feature vectors were composed with the simple extracted pitch-related, intensity related, and duration related attributes, such as maximum, minimum, median, range and variability values. However, researchers are still debating what features influence the recognition of emotion...

In recent years, emotion recognition has attracted extensive interest in signal processing, artificial intelligence and pattern recognition due to its potential applications to human-computer-interaction (HCI). Most previously published works in the field of emotion recognition devote to performing emotion recognition by using either affective speech or facial expression. However, Affective spe...