| Title |
DAVID: An open-source platform for real-time transformation of infra-segmental emotional cues in running speech
|
|---|---|
| Published in |
Behavior Research Methods, April 2017
|
| DOI | 10.3758/s13428-017-0873-y |
| Pubmed ID | |
| Authors |
Laura Rachman, Marco Liuni, Pablo Arias, Andreas Lind, Petter Johansson, Lars Hall, Daniel Richardson, Katsumi Watanabe, Stéphanie Dubal, Jean-Julien Aucouturier |
| Abstract |
We present an open-source software platform that transforms emotional cues expressed by speech signals using audio effects like pitch shifting, inflection, vibrato, and filtering. The emotional transformations can be applied to any audio file, but can also run in real time, using live input from a microphone, with less than 20-ms latency. We anticipate that this tool will be useful for the study of emotions in psychology and neuroscience, because it enables a high level of control over the acoustical and emotional content of experimental stimuli in a variety of laboratory situations, including real-time social situations. We present here results of a series of validation experiments aiming to position the tool against several methodological requirements: that transformed emotions be recognized at above-chance levels, valid in several languages (French, English, Swedish, and Japanese) and with a naturalness comparable to natural speech. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 20% |
| United States | 1 | 10% |
| Sri Lanka | 1 | 10% |
| Unknown | 6 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 2 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 78 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 17% |
| Student > Master | 13 | 17% |
| Researcher | 11 | 14% |
| Student > Bachelor | 8 | 10% |
| Professor > Associate Professor | 6 | 8% |
| Other | 12 | 15% |
| Unknown | 15 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 23 | 29% |
| Computer Science | 11 | 14% |
| Linguistics | 6 | 8% |
| Engineering | 4 | 5% |
| Neuroscience | 4 | 5% |
| Other | 11 | 14% |
| Unknown | 19 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 December 2020.
All research outputs
#5,577,740
of 25,998,826 outputs
Outputs from Behavior Research Methods
#758
of 2,589 outputs
Outputs of similar age
#90,612
of 327,572 outputs
Outputs of similar age from Behavior Research Methods
#11
of 44 outputs
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We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.