Speech signal in addition to the linguistic information contains additional information about the speaker: age, gender, social status, accent (foreign accent, dialects, etc.), emotional state, health etc. Some of these informational channels induce changes of the speech acoustic characteristics. This article presents evaluation of the ASR acoustic models (first trained on neutral, read speech) ...

Speech signal in addition to the linguistic information contains additional information about the speaker: age, gender, social status, accent (foreign accent, dialects, etc.), emotional state, health etc. Some of these informational channels induce changes of the speech acoustic characteristics. This article presents evaluation of the ASR acoustic models (first trained on neutral, read speech) ...

Emotional speech recognition has been studied using different approaches, which some works use real emotions and other uses acted ones, usually real emotional speech databases include like two or three emotions and acted ones have five or more, for this work Berlin Emotional Speech Database [1] was selected due to its availability, which has 535 sentences expressed in seven emotions (anger, bor...

This paper describes a technique for modeling and controlling emotional expressivity of speech in HMM-based speech synthesis. A problem of conventional emotional speech synthesis based on HMM is that the intensity of an emotional expression appearing in synthetic speech completely depends on the database used for model training. To take into account the emotional expressivity that listeners act...

A database of recordings of Danish Emotional Speech, DES, has been recorded and analysed. DES has been collected in order to evaluate how well the emotional state in emotional speech is identified by humans. The results sets a standard for identifying Danish emotional speech. DES contains recordings from four actors, two of each gender. Actors were used for the recordings as they were believed ...

The determination of an emotional state through speech increases the amount of information associated with a speaker. It is therefore important to be able to detect and identify a speaker's emotional state or state of stress. Various techniques are used in the literature to classify emotional/stressed states on the basis of speech, often using di erent speech feature vectors at the same time. T...

speech emotion can add more information to speech in comparison to available textual information. however, it will also lead to some problems in speech recognition process. in a previous study, we depicted the substantial changes of speech parameters caused by speech emotion. therefore, in order to improve emotional speech recognition rate, in a first step, the effects of emotion on speech par...

Speech emotional database and recognition is the challenging part of human computer interaction. The current research focuses towards the detection of emotion in various situations, while the database demands more to fetch out the work of recognition. The study investigates the various existing speech databases containing various basic emotions, enhancing the appropriate database development as...

Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction (HCI). In this paper, we recognize three emotional states: happy, sad and neutral. The explored features include: energy, pitch, linear predictive spectrum coding (LPCC), mel-frequency spectrum coefficients (MFCC), and mel-energy spectrum dynamic coefficients (MEDC). A German Corpus (Berlin Datab...

Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction (HCI). In this paper, we recognize three emotional states: happy, sad and neutral. The explored features include: energy, pitch, linear predictive spectrum coding (LPCC), Mel-frequency spectrum coefficients (MFCC), and Mel-energy spectrum dynamic coefficients (MEDC). A German Corpus (Berlin Datab...