Paper Info
Title
Modelling phonetic context using head-body-tail models for connected digit recognition
Abstract
Both whole word modelling and context modelling have proven to improve recognition performance for connected digit strings. In this paper we will show that word boundary variation can be effectively modelled by applying the Head-Body-Tail (HBT) method as proposed by Chou et al in (1) and also applied by Gandhi in (2). Each digit is split into three parts, representing the beginning, middle and end of a word. The middle part - the body - is assumed to be context-independent, whereas the first part - the head - and the last part - the tail - incorporate information about the preceding or subsequent digit. The results we obtained with HBT-modelling are compared with results obtained with whole-word models (WWM's) (3) and with the results obtained with HBT-models reported in (2). It is shown that using HBT models a relative improvement over context- independent WWM's of 28% on string level can be reached.
Year
Venue
Field
2000
INTERSPEECH
Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Natural language processing,Digit recognition
DocType
Citations 
PageRank 
Conference
6
0.54
References 
Authors
3
2
Name
Order
Citations
PageRank
Janienke Sturm135636.54
Eric Sanders213827.90