Name
Abstract | ||
|---|---|---|
Conventionally, automatic assessment of pathological speech involves two main steps: (1) extraction of pathology-specific features; (2) classification or regression of extracted features. Given the great variety of speech and language disorders, feature design is never a straightforward task, and yet it is most critical to the performance of assessment. This paper presents an end-to-end approach to automatic speech assessment for Cantonese-speaking people with aphasia (PWA). The assessment is formulated as a binary classification problem to differentiate PWA with high scores of subjective assessment from those with low scores. The sequence-to-one GRU-RNN and CNN models are applied to realize the end-to-end mapping from speech signals to the classification result. The speech features used for assessment are learned implicitly by the neural network model. Preliminary experimental results show that the end-to-end approach could reach a performance level comparable to conventional two-step approach. The experimental results also suggest that CNN performs better than sequence-toone GRU-RNN in this specific task. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISCSLP.2018.8706690 | 2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP) |
Keywords | Field | DocType |
Feature extraction,Task analysis,Training,Neural networks,Mel frequency cepstral coefficient,Databases | Mel-frequency cepstrum,Task analysis,Regression,Binary classification,End-to-end principle,Computer science,Aphasia,Speech recognition,Feature extraction,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-5386-5627-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Qin | 1 | 1 | 5.43 |
| Tan Lee | 2 | 476 | 74.69 |
| Yuzhong Wu | 3 | 0 | 0.68 |
| anthony pak hin kong | 4 | 0 | 2.03 |