Paper Info
Title
Design of multi-feature class models for Speech Recognition Security systems with under-resourced languages
Abstract
One of the goals of Speech Recognition Security (SRS) systems is to have appropriately tools to recognize speech password spoken based on elements such as words, sub-word or speakers. The main goal of the present work is to design robust ASR systems based on alternative ways to the classical evaluation rates, which often depend on the vocabulary of the task and on the language resources available. The drawback of this approach is that it is not straightforward that a system with a slightly lower WER during tests will adapt properly to new utterances, and this is much more sensible when the baseline system has a big error rate since there are many features that could be improved. This tends to be the case of under-resourced languages, since the lack of resources has a great impact in the performance of the system and not all the standard methods are suitable to any kind of language or task. The novel approach is to choose balanced multi-features of the acoustic models and the sub-word units based on rates related to entropy, mutual information and similitude. Selected models are integrated in an ontology-driven Audio Information Retrieval system that suits the requirements of under-resourced languages.
Year
DOI
Venue
2011
10.1109/CCST.2011.6095947
2011 Carnahan Conference on Security Technology
Keywords
Field
DocType
Security Systems,Under-resourced languages,multi-feature class modelling,fuzzy validation indexes
Similitude,Computer science,Word error rate,Robustness (computer science),Speech recognition,Speaker recognition,Natural language processing,Password,Artificial intelligence,Audio signal processing,Hidden Markov model,Vocabulary
Conference
ISSN
ISBN
Citations 
1071-6572
978-1-4577-0902-9
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Nora Barroso18213.75
K. López De Ipiña2205.23
Carmen Hernández333.49
Aitzol Ezeiza410117.59