Name
Abstract | ||
|---|---|---|
In this paper, we present a novel approach to the classification of speaker likability, that is, a measure of how pleasant a given speaker is to listen to. Instead of blindly extracting a large number of features, we identify a small set of features which represent perceptual speech characteristics. This set of features is sent to a linear support vector machine to perform speaker likability classification. We train and evaluate the performance of our algorithm on the Interspeech 2012 speaker trait challenge database and we show that our likability classifier achieves an absolute improvement of 3.2% over the baseline classifier developed for the challenge while considerably reducing the number of features needed. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6639322 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
feature extraction,pattern classification,performance evaluation,speech processing,support vector machines,Interspeech 2012 speaker trait challenge database,feature extraction,linear support vector machine,perceptual speech represent characteristics,performance evaluation,speaker likability classification,Speaker traits,classification,likability | Speech processing,Pattern recognition,Computer science,Support vector machine,Feature extraction,Speech recognition,Speaker recognition,Artificial intelligence,Speaker diarisation,Classifier (linguistics),Perception,Small set | Conference |
ISSN | Citations | PageRank |
1520-6149 | 2 | 0.42 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sira Gonzalez | 1 | 2 | 0.42 |
| Xavier Anguera | 2 | 2 | 0.42 |