Neural network classifier architectures for phoneme recognition / by William Treurniet.: Co24-3/8-1992-1E-PDF
"Automatic recognition of words in continuous speech is difficult to do with whole-word template models, especially when the vocabulary is reasonably large. Instead, the currently preferred approaches for this task hypothesize the presence of words or sub-words, such as syllables or phonemes, on the basis of likelihood estimates obtained from comparisons of the acoustic data with statistical models derived from training data. This paper is concerned with the application of artificial neural networks, trained with the back-propagation learning algorithm, to modelling phonemes extracted from the DARPA TIMIT multi-speaker, continuous speech data base"--Abstract.
Permanent link to this Catalogue record:
publications.gc.ca/pub?id=9.890766&sl=0
| Department/Agency |
|
|---|---|
| Title | Neural network classifier architectures for phoneme recognition / by William Treurniet. |
| Series title |
|
| Publication type | Monograph - View Master Record |
| Language | [English] |
| Format | Digital text |
| Electronic document | |
| Note(s) |
|
| Publishing information |
|
| Author / Contributor |
|
| Description | 1 online resource (iii, 38 pages) : illustrations. |
| Catalogue number |
|
| Subject terms |
Request alternate formats
To request an alternate format of a publication, complete the Government of Canada Publications email form. Use the form’s “question or comment” field to specify the requested publication.Page details
- Date modified: