Speech emotion recognition, as a vital part of affective human computer interaction, has become a new challenge to speech processing. In this paper, a hybrid of hidden Markov models (HMMs) and artificial neural network (ANN) has been proposed to classify emotions, combining advantage on capability to dynamic time warping of HMM and pattern recognition of ANN. HMMs, which export likelihood proba...

This paper investigates the use of speech-to-text methods for assigning an emotion class to a given speech utterance. Previous work shows that an emotion extracted from text can convey complementary evidence to the information extracted by classifiers based on spectral, or other non-linguistic features. As speech-to-text usually presents significantly more computational effort, in this study we...

Speech and emotion recognition improve the quality of human computer interaction and allow easier to use interfaces for every level of user in software applications. In this study, we have developed two different neural networks called emotion recognition neural network (ERNN) and Gram-Charlier emotion recognition neural network (GERNN) to classify the voice signals for emotion recognition. The...

Synthesizing emotional speech by means of conversion from neutral speech allows us to generate emotional speech from many existing Text-to-Speech (TTS) systems. How much of the target emotion can be portrayed by the generated speech is largely dependent on the emotion data used to train the mapping function for voice transformation. In this paper, we introduce a method to pre-process the emotio...

Speech emotion recognition is an important problem with applications as varied as human-computer interfaces and affective computing. Previous approaches to emotion recognition have mostly focused on extraction of carefully engineered features and have trained simple classifiers for the emotion task. There has been limited effort at representation learning for affect recognition, where features ...

In this paper, we describe our approach of enabling an interactive dialogue system to recognize user emotion and sentiment in realtime. These modules allow otherwise conventional dialogue systems to have “empathy” and answer to the user while being aware of their emotion and intent. Emotion recognition from speech previously consists of feature engineering and machine learning where the first s...

One of the largest challenges in speaker recognition is dealing with speaker-emotion variability problem. Nowadays, compensation techniques are the main solutions to this problem. In these methods, all kinds of speakers’ emotion speech should be elicited thus it is not user-friendly in the application. Therefore the basic problem is how to get the distribution of speakers’ emotion speech and ho...

We present EmoVoice, a framework for emotional speech corpus and classifier creation and for offline as well as real-time online speech emotion recognition. The framework is intended to be used by non-experts and therefore comes with an interface to create an own personal or application specific emotion recogniser. Furthermore, we describe some applications and prototypes that already use our f...