Chapter title |
Recognizing Emotional States Using Speech Information
|
---|---|
Chapter number | 13 |
Book title |
GeNeDis 2016
|
Published in |
Advances in experimental medicine and biology, October 2017
|
DOI | 10.1007/978-3-319-57348-9_13 |
Pubmed ID | |
Book ISBNs |
978-3-31-957347-2, 978-3-31-957348-9
|
Authors |
Michalis Papakostas, Giorgos Siantikos, Theodoros Giannakopoulos, Evaggelos Spyrou, Dimitris Sgouropoulos, Papakostas, Michalis, Siantikos, Giorgos, Giannakopoulos, Theodoros, Spyrou, Evaggelos, Sgouropoulos, Dimitris |
Abstract |
Emotion recognition plays an important role in several applications, such as human computer interaction and understanding affective state of users in certain tasks, e.g., within a learning process, monitoring of elderly, interactive entertainment etc. It may be based upon several modalities, e.g., by analyzing facial expressions and/or speech, using electroencephalograms, electrocardiograms etc. In certain applications the only available modality is the user's (speaker's) voice. In this paper we aim to analyze speakers' emotions based solely on paralinguistic information, i.e., not depending on the linguistic aspect of speech. We compare two machine learning approaches, namely a Convolutional Neural Network and a Support Vector Machine. The former is trained using raw speech information, while the latter is trained on a set of extracted low-level features. Aiming to provide a multilingual approach, training and testing datasets contain speech from different languages. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 33 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 8 | 24% |
Student > Master | 7 | 21% |
Researcher | 3 | 9% |
Student > Ph. D. Student | 2 | 6% |
Student > Postgraduate | 2 | 6% |
Other | 2 | 6% |
Unknown | 9 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 9 | 27% |
Engineering | 8 | 24% |
Medicine and Dentistry | 2 | 6% |
Neuroscience | 2 | 6% |
Linguistics | 1 | 3% |
Other | 1 | 3% |
Unknown | 10 | 30% |