Name
Abstract | ||
|---|---|---|
The ability to accurately perceive whether a speaker is asking a question or is making a statement is crucial for any successful interaction. However, learning and classifying tonal patterns has been a challenging task for automatic speech recognition and for models of tonal representation, as tonal contours are characterized by significant variation. This paper provides a classification model of Cypriot Greek questions and statements. We evaluate two state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.36505/exling-2017/08/0005/000307 | CoRR |
DocType | Volume | Citations |
Journal | abs/1706.03952 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-Philippe Bernardy | 1 | 0 | 3.38 |
| Charalambos Themistocleous | 2 | 0 | 0.34 |