Automatic Speech Recognition fails to a certain extent when confronted with highly affective speech. In order to cope with this problem we suggest dynamic adaptation to the actual user emotion. The ASR framework is built by a hybrid ANN/HMM mono-phone 5k bi-gram LM recognizer. Based hereon we show adaptation to the affective speaking style. Speech emotion recognition takes place prior to the ac...

Robots that can recognize emotions can improve humans’ mental health by providing empathy and social communication. Emotion recognition by robots is challenging because unlike in human-computer environments, facial information is not always available. Instead, our method proposes using speech and gait analysis to recognize human emotion. Previous research suggests that the dynamics of emotional...

In the field of human computer interaction automatic speech emotion recognition is a current research topic. Emotion recognition in speech is a challenging problem because it is unclear that which features are effective for speech emotion recognition. In this paper we proposed an approach in which we extract the features of energy, spectral and acoustic domains and then merging these features b...

Speech and emotion recognition improve the quality of human computer interaction and allow more easy to use interfaces for every level of user in software applications. In this study, we have developed the emotion recognition neural network (ERNN) to classify the voice signals for emotion recognition. The ERNN has 128 input nodes, 20 hidden neurons, and three summing output nodes. A set of 9793...

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...

Emotion recognition from human speech is a challenge for the researchers. It is mostly considered under ideal acoustic conditions. The performance of such system is degraded while there is existence of environmental mismatches between training and testing phases. For robust speech recognition it requires for reduction of redundancy, variability, and capturing ability of speech signals in noisy ...

As a hot topic of speech signal processing, speech emotion recognition methods have been developed rapidly in recent years. Some satisfactory results have been achieved. However, it should be noted that most of these methods are trained and evaluated on the same corpus. In reality, the training data and testing data are often collected from different corpora, and the feature distributions of di...

In this paper, we describe emotion recognition experiments car­ ried out for spontaneous affective speech with the aim to com­ pare the added value of annotation of felt emotion versus an­ notation of perceived emotion. Using speech material avail­ able in the TNO-GAMING corpus (a corpus containing audio­ visual recordings of people playing videogames), speech-based affect recognizers were deve...

Emotion plays an important role in human communications. We construct a framework for multi-modal fusion emotion recognition. Facial expression features and speech features are respectively extracted from image sequences and speech signals. In order to locate and track facial feature points, we construct an Active Appearance Model for facial images with all kinds of expressions. Facial Animatio...

Speech Emotion Recognition (SER) makes it possible for machines to perceive affective information. Our previous research differed from conventional SER endeavours in that focused on recognising unseen emotions speech autonomously through machine learning. Such a step would enable the automatic leaning of unknown emerging emotional states. This type learning framework, however, still relied manu...