Name
Abstract | ||
|---|---|---|
Progress of automatic speech recognition systems' (ASR) development is, inter alia, made by using signal representation sensitive for more and more sophisticated features. This paper is an overview of our investigation of the new context-sensitive speech signal's representation, based on wavelet-Fourier transform (WFT), and proposal of it's quality measures. The paper is divided into 5 sections, introducing as follows: phonetic-acoustic contextuality in speech, basics of WFT, WFT speech signal feature space, feature space quality measures and finally conclusion of our achievements. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/3-540-33521-8_10 | INTELLIGENT INFORMATION PROCESSING AND WEB MINING, PROCEEDINGS |
Field | DocType | ISSN |
Feature vector,Computer science,Decomposition tree,Speech recognition,Artificial intelligence,Machine learning,Kochen–Specker theorem | Conference | 1615-3871 |
Citations | PageRank | References |
2 | 0.38 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jakub Galka | 1 | 44 | 7.47 |
| Michał Kępiński | 2 | 2 | 0.38 |