Name
Abstract | ||
|---|---|---|
The improvement achieved by changing the basis of speech recognition from words to morphs (various sub-word units) varies greatly across tasks and languages. We make an attempt to explore the source of this variability by the investigation of three LVCSR tasks corresponding to three speech genres of a highly agglutinative language. Novel, press conference and broadcast news transcription results are presented and compared to spontaneous speech recognition results in several experimental setups. A noticeable correlation is observed between an easily computable characteristic of various language speech recognition tasks and between the relative improvements due to (statistical) morph-based approaches. |
Year | Venue | Keywords |
|---|---|---|
2009 | INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5 | speech recognition, rich morphology, morph, language modeling, LVCSR, vocabulary growth |
Field | DocType | Citations |
Speech corpus,Computer science,Viseme,Speech recognition,Speaker recognition,Speech technology,Acoustic model | Conference | 3 |
PageRank | References | Authors |
0.45 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Péter Mihajlik | 1 | 58 | 10.15 |
| Balázs Tarján | 2 | 21 | 4.92 |
| Zoltán Tüske | 3 | 119 | 17.32 |
| Tibor Fegyó | 4 | 61 | 10.46 |