This paper presents a technique for synthesizing emotional speech based on an emotion-independent model which is called “average emotion” model. The average emotion model is trained using a multi-emotion speech database. Applying a MLLR-based model adaptation method, we can transform the average emotion model to present the target emotion which is not included in the training data. A multi-emot...

In this work we will introduce EmoSTAR as a new emotional database and perform cross-corpus tests between EmoSTAR and EmoDB (Berlin Emotional Database) using one of the two databases as training set and the other as test set. We will also investigate the performance of feature selectors in both databases. Feature extraction will be implemented with openSMILE toolkit employing Emobase and Emo_la...

This research work presents a new valid dataset for Punjabi called the Emotional Speech Database (PEMO) which has been developed to assess ability recognize emotions in speech by both computers and humans. The PEMO includes samples from about 60 speakers with an age range between 20 45 years, four fundamental emotions, including anger, sad, happy neutral. In order create data, films are retriev...

In this paper, we investigate cross-lingual automatic speech emotion recognition. The basic idea is that since the emotion recognition system is based on the acoustic features only, it is possible to combine data in different languages to improve the recognition accuracy. We begin with the construction of a Mandarin database of emotional speech, which is similar to the well-known Berlin Databas...

The present research focuses on the development of a fear detection system for surveillance applications based on acoustic cues. The emotional speech material used for this study comes from the previously collected SAFE Database (Situation Analysis in a Fictional and Emotional Database) which consists of audiovisual sequences extracted from movie fictions. We address here the question of a spec...

Affect or emotion classification from speech has much to benefit from ensemble classification methods. In this paper we apply a simple voting mechanism to an ensemble of classifiers and attain a modest performance increase compared to the individual classifiers. A natural emotional speech database was compiled from 11 speakers. Listener-judges were used to validate the emotional content of the ...

This paper describes a system to synthesize emotional speech based on TDPLOSA. The system has a database of VCV (vowel consonant vowel) segments for each of three emotions; anger, sadness and joy. These segments have emotional speech quality. The database contains four kinds of VCV segments which are prosodically balanced in the sense that their concatenation can generate any accent patterns of...

An approach for the recognition of emotions in speech is presented. The target language is Mexican Spanish, and for this purpose a speech database was created. The approach consists in the phoneme acoustic modelling of emotion-specific vowels. For this, a standard phoneme-based Automatic Speech Recognition (ASR) system was built with Hidden Markov Models (HMMs), where different phoneme HMMs wer...

Communicative goals are simultaneously expressed through gestures and speech to convey messages enriched with valuable verbal and non-verbal clues. This paper analyzes and quantifies how linguistic and affective goals are reflected in facial expressions. Using a database recorded from an actress with markers attached to her face, the facial features during emotional speech were compared with th...

In the present work we address the problem of phone duration modeling for the needs of emotional speech synthesis. Specifically, relying on ten well known machine learning techniques, we investigate the practical usefulness of two feature selection techniques, namely the Relief and the Correlation-based Feature Selection (CFS) algorithms, for improving the accuracy of phone duration modeling. T...