@inproceedings{d96a1f4ddfef48a0b65902897d52ab6e,
title = "Improving the performance of out-of-vocabulary word rejection by using support vector machines",
abstract = "Support Vector Machines (SVM) represents a new approach to pattern classification developed from the theory of structural risk minimization [1]. In this paper, we propose an approach to improve the performance of confidence measurements for out-of-vocabulary word rejection by using SVM. Confidence measures are computed from the information of n-best candidates and anti-word by a Hidden Markov Model (HMM) based speech recognizer. The acceptance/rejection decision for a word is based on the confidence score which is provided by SVM classifier. And the decision is performed for each word in vocabulary separately. The performance of the proposed SVM classifier is compared with method based on posterior probability and anti-word probability. Experiments of Mandarin command recognition have showed that better performance can be obtained when using the proposed method.",
keywords = "Confidence measure, Speech recognition, Support vector machines",
author = "Shilei Huang and Xiang Xie and Jingming Kuang",
year = "2006",
language = "English",
isbn = "9781604234497",
series = "INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP",
publisher = "International Speech Communication Association",
pages = "1618--1621",
booktitle = "INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP",
note = "INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP ; Conference date: 17-09-2006 Through 21-09-2006",
}