Abstract
| Original language | English |
|---|---|
| Title of host publication | Advances in Speech and Language Technologies for Iberian Languages |
| Subtitle of host publication | Third International Conference, IberSPEECH 2016, Lisbon, Portugal, November 23-25, 2016, Proceedings |
| Editors | Alberto Abad, Alfonso Ortega, António Teixeira, Carmen García Mateo, Carlos D. Martínez Hinarejos, Fernando Perdigão, Fernando Batista, Nuno Mamede |
| Publisher | Springer Nature Switzerland AG |
| Pages | 182-191 |
| ISBN (Electronic) | 978-3-319-49169-1 |
| ISBN (Print) | 978-3-319-49168-4 |
| DOIs | |
| Publication status | Published - Nov 2016 |
| Externally published | Yes |
| Event | International Conference on Advances in Speech and Language Technologies for Iberian Languages - Lisbon, Portugal Duration: 23 Nov 2016 → 25 Nov 2016 https://link.springer.com/conference/iberspeech |
Publication series
| Name | Lecture Notes in Computer Science book series |
|---|---|
| Volume | volume 10077 |
| Name | Lecture Notes in Artificial Intelligence book sub series |
|---|---|
| Volume | volume 10077 |
Conference
| Conference | International Conference on Advances in Speech and Language Technologies for Iberian Languages |
|---|---|
| Abbreviated title | IberSPEECH 2016 |
| Country | Portugal |
| City | Lisbon |
| Period | 23/11/16 → 25/11/16 |
| Internet address |
Fingerprint
Keywords
- Hyperarticulation
- Speech
- Let’s Go
Cite this
}
Automatic Detection of Hyperarticulated Speech. / Ribeiro, Eugenio; Batista, Fernando; Trancoso, Isabel; Ribeiro, Ricardo; de Matos, David.
Advances in Speech and Language Technologies for Iberian Languages: Third International Conference, IberSPEECH 2016, Lisbon, Portugal, November 23-25, 2016, Proceedings. ed. / Alberto Abad; Alfonso Ortega; António Teixeira; Carmen García Mateo; Carlos D. Martínez Hinarejos; Fernando Perdigão; Fernando Batista; Nuno Mamede. Springer Nature Switzerland AG, 2016. p. 182-191 (Lecture Notes in Computer Science book series; Vol. volume 10077), (Lecture Notes in Artificial Intelligence book sub series; Vol. volume 10077).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review
TY - GEN
T1 - Automatic Detection of Hyperarticulated Speech
AU - Ribeiro, Eugenio
AU - Batista, Fernando
AU - Trancoso, Isabel
AU - Ribeiro, Ricardo
AU - de Matos, David
PY - 2016/11
Y1 - 2016/11
N2 - Hyperarticulation is a speech adaptation that consists of adopting a clearer form of speech in an attempt to improve recognition levels. However, it has the opposite effect when talking to ASR systems, as they are not trained with such kind of speech. We present approaches for automatic detection of hyperarticulation, which can be used to improve the performance of spoken dialog systems. We performed experiments on Let’s Go data, using multiple feature sets and two classification approaches. Many relevant features are speaker dependent. Thus, we used the first turn in each dialog as the reference for the speaker, since it is typically not hyperarticulated. Our best results were above 80 % accuracy, which represents an improvement of at least 11.6 % points over previously obtained results on similar data. We also assessed the classifiers’ performance in scenarios where hyperarticulation is rare, achieving around 98 % accuracy using different confidence thresholds.
AB - Hyperarticulation is a speech adaptation that consists of adopting a clearer form of speech in an attempt to improve recognition levels. However, it has the opposite effect when talking to ASR systems, as they are not trained with such kind of speech. We present approaches for automatic detection of hyperarticulation, which can be used to improve the performance of spoken dialog systems. We performed experiments on Let’s Go data, using multiple feature sets and two classification approaches. Many relevant features are speaker dependent. Thus, we used the first turn in each dialog as the reference for the speaker, since it is typically not hyperarticulated. Our best results were above 80 % accuracy, which represents an improvement of at least 11.6 % points over previously obtained results on similar data. We also assessed the classifiers’ performance in scenarios where hyperarticulation is rare, achieving around 98 % accuracy using different confidence thresholds.
KW - Hyperarticulation
KW - Speech
KW - Let’s Go
U2 - 10.1007/978-3-319-49169-1_18
DO - 10.1007/978-3-319-49169-1_18
M3 - Conference article in proceeding
SN - 978-3-319-49168-4
T3 - Lecture Notes in Computer Science book series
SP - 182
EP - 191
BT - Advances in Speech and Language Technologies for Iberian Languages
A2 - Abad, Alberto
A2 - Ortega, Alfonso
A2 - Teixeira, António
A2 - García Mateo, Carmen
A2 - D. Martínez Hinarejos, Carlos
A2 - Perdigão, Fernando
A2 - Batista, Fernando
A2 - Mamede, Nuno
PB - Springer Nature Switzerland AG
ER -