High-performance low-complexity wordspotting using neural networks

A high-performance low-complexity neural network wordspotter was developed using radial basis function (RBF) neutral networks in a hidden Markov model (HMM) framework. Two new complementary approaches substantially improve performance on the talker-independent Switchboard corpus. Figure of merit (FO...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on signal processing 1997-11, Vol.45 (11), p.2864-2870
Hauptverfasser:	Chang, E.I., Lippmann, R.P.
Format:	Artikel
Sprache:	eng
Schlagworte:	Acoustic signal detection Applied sciences Artificial intelligence Computer science control theory systems Connectionism. Neural networks Control systems Exact sciences and technology Hidden Markov models Maximum likelihood detection Measurement Neural networks NIST Speech Speech and sound recognition and synthesis. Linguistics Testing Training data
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!