Automatic speech recognition can fail to a certain extent when confronted with emotionally distorted speech. Great efforts have been spent so far to cope with noise conditions or speaker’s characteristics. Yet, adaptation to the emotional condition of the speaker could help to further improve the overall performance. In this respect we aim at a robust and reliable recognition of the speaker’s e...

The paper presents an emotional speech recognition system with the analysis of manifolds of speech. Working with large volumes of high-dimensional acoustic features, the researchers confront the problem of dimensionality reduction. Unlike classical techniques, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), a new approach, named Enhanced Lipschitz Embedding (E...

In speaker recognition applications, the changes of emotional states are main causes of errors. The ongoing work described in this contribution attempts to enhance the performance of automatic speaker recognition (ASR) systems on emotional speech. Two procedures that only need a small quantity of affective training data are applied to ASR task, which is very practical in real-world situations. ...

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...

The variations of speech parameters due to emotion or stress are noticeable. In the presence of such variations, if a neutral model is used for the system, the speech recognition accuracy deteriorates. The evaluation of how emotion influences speech parameters is the first step towards emotional speech recognition. Pitch frequency is an important parameter in speech processing systems. Therefor...

in recent years, sub-band speech recognition has been found useful in addressing the need for robustness in speech recognition, especially for the speech contaminated by band-limited noise. in sub-band speech recognition, the full band speech is divided into several frequency sub-bands, with the result of the recognition task given by the combination of the sub-band feature vectors or their lik...

Speech carries information not only about the lexical content, but also about the age, gender, signature and emotional state of the speaker. Speech in different emotional states is accompanied bydistinct changes in the productionmechanism. In this chapter, we present a review of analysis methods used for emotional speech. In particular, we focus on the issues in data collection, feature represe...

Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences. However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks. In this paper, a novel ...