Change search
ReferencesLink to record
Permanent link

Direct link
Comparing phoneme and feature based speech recognition using artificial neural networks
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
1992 (English)Conference paper (Refereed)
Abstract [en]

An artificial neural network has been trained by the error backpropagation technique to recognise phonemes and words. The speech material was recorded by a male Swedish talker and was labelled by a phonetician. There were 38 output nodes corresponding to Swedish phonemes. The training algorithm was somewhat modified to increase the training speed. Introducing coarticulation information by adding simple recurrency to the net is shown to more effective than expanding the size of the input spectral window. The phoneme recognition network was used with dynamic programming for time alignment to recognise connected digits. It was compared to a similar recogniser based on nine quasi-phonetic features instead of 38 phonemes. The phoneme based system performed better than the feature based one. I.

Place, publisher, year, edition, pages
1992. 1279-1282 p.
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-91464OAI: diva2:510363
Proceedings ICSLP 92
NR 20140805Available from: 2012-03-15 Created: 2012-03-15Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Blomberg, Mats
By organisation
Speech, Music and Hearing, TMH
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 18 hits
ReferencesLink to record
Permanent link

Direct link