In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose. It contains data for male and female actors in English and a male actor in French. The database covers 5 emotion classes so it could be suitable to build synthesis and voice transformation systems with the potential to control the emotional dimension in a continuous way. We show the data's efficiency by building a simple MLP system converting neutral to angry speech style and evaluate it via a CMOS perception test. Even though the system is a very simple one, the test show the efficiency of the data which is promising for future work.
The CREMA-D database @cite_15 is similar to the RAVDESS. For this database, 12 different sentences were recorded by 91 different actors, for the 6 basic emotions:happy, sad, anger, fear, disgust, and neutral. Only one of the 12 sentences was expressed in 3 different intensities, for the other 11, the intensity was not specified. The authors report 7442 files in total. This database was also validated through perception tests and helped validate the emotion category and intensity.
@cite_15
2803193013 2030931454 1995642379 2108581428
The RAVDESS is a validated multimodal database of emotional speech and song. The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of 7356 recordings were each rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity and test-retest intrarater reliability were reported. Corrected accuracy and composite "goodness" measures are presented to assist researchers in the selection of stimuli. All recordings are made freely available under a Creative Commons license and can be downloaded at https: doi.org 10.5281 zenodo.1188976. People convey their emotional state in their face and voice. We present an audio-visual dataset uniquely suited for the study of multi-modal emotion expression and perception. The dataset consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral). 7,442 clips of 91 actors with diverse ethnicbackgrounds were rated by multiple raters in three modalities: audio, visual, and audio-visual. Categorical emotion labels andreal-value intensity values for the perceived emotion were collected using crowd-sourcing from 2,443 raters. The human recognition of intended emotion for the audio-only, visual-only, and audio-visual data are 40.9, 58.2 and 63.6 percent respectively. Recognition rates are highest for neutral, followed by happy, anger, disgust, fear, and sad. Average intensity levels of emotion are rated highest forvisual-only perception. The accurate recognition of disgust and fear requires simultaneous audio-visual cues, while anger andhappiness can be well recognized based on evidence from a single modality. The large dataset we introduce can be used to probe other questions concerning the audio-visual perception of emotion. Research on emotional speech often requires valid stimuli for assessing perceived emotion through prosody and lexical content. To date, no comprehensive emotional speech database for Persian is officially available. The present article reports the process of designing, compiling, and evaluating a comprehensive emotional speech database for colloquial Persian. The database contains a set of 90 validated novel Persian sentences classified in five basic emotional categories (anger, disgust, fear, happiness, and sadness), as well as a neutral category. These sentences were validated in two experiments by a group of 1,126 native Persian speakers. The sentences were articulated by two native Persian speakers (one male, one female) in three conditions: (1) congruent (emotional lexical content articulated in a congruent emotional voice), (2) incongruent (neutral sentences articulated in an emotional voice), and (3) baseline (all emotional and neutral sentences articulated in neutral voice). The speech materials comprise about 470 sentences. The validity of the database was evaluated by a group of 34 native speakers in a perception test. Utterances recognized better than five times chance performance (71.4 ) were regarded as valid portrayals of the target emotions. Acoustic analysis of the valid emotional utterances revealed differences in pitch, intensity, and duration, attributes that may help listeners to correctly classify the intended emotion. The database is designed to be used as a reliable material source (for both text and speech) in future cross-cultural or cross-linguistic studies of emotional speech, and it is available for academic research purposes free of charge. To access the database, please contact the first author. This paper describes an experimental study on the detection of emotion from speech. As computer-based characters such as avatars and virtual chat faces become more common, the use of emotion to drive the expression of the virtual characters becomes more important. This study utilizes a corpus containing emotional speech with 721 short utterances expressing four emotions: anger, happiness, sadness, and the neutral (unemotional) state, which were captured manually from movies and teleplays. We introduce a new concept to evaluate emotions in speech. Emotions are so complex that most speech sentences cannot be precisely assigned to a particular emotion category; however, most emotional states nevertheless can be described as a mixture of multiple emotions. Based on this concept we have trained SVMs (support vector machines) to recognize utterances within these four categories and developed an agent that can recognize and express emotions.