Gaussian Mixture Language Models for Speech Recognition

We propose a Gaussian mixture language model for speech recognition. Two potential benefits of using this model are smoothing unseen events, and ease of adaptation. It is shown how this model can be used alone or in conjunction with a a conventional N-gram model to calculate word probabilities. An i...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Afify, M., Siohan, O., Sarikaya, R.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We propose a Gaussian mixture language model for speech recognition. Two potential benefits of using this model are smoothing unseen events, and ease of adaptation. It is shown how this model can be used alone or in conjunction with a a conventional N-gram model to calculate word probabilities. An interesting feature of the proposed technique is that many methods developed for acoustic models can be easily ported to GMLM. We developed two implementations of the proposed model for large vocabulary Arabic speech recognition with results comparable to conventional N-gram.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2007.367155