Combining outputs of multiple LVCSR models by machine learning

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Combining outputs of multiple LVCSR models by machine learning

This paper proposes to apply machine learning techniques to the task of combining outputs of multiple LVCSR models, where, as features of machine learning, information such as the models which output the hypothesized word, its part-of-speech, and its syllable length are useful for improving the word recognition rate. Experimental results show that the combination result outperforms several base...

متن کامل

Combining Outputs from Multiple Machine Translation Systems

Currently there are several approaches to machine translation (MT) based on different paradigms; e.g., phrasal, hierarchical and syntax-based. These three approaches yield similar translation accuracy despite using fairly different levels of linguistic knowledge. The availability of such a variety of systems has led to a growing interest toward finding better translations by combining outputs f...

متن کامل

An Empirical Study on Multiple LVCSR Model Combination by Machine Learning

This paper proposes to apply machine learning techniques to the task of combining outputs of multiple LVCSR models. The proposed technique has advantages over that by voting schemes such as ROVER, especially when the majority of participating models are not reliable. In this machine learning framework, as features of machine learning, information such as the model IDs which output the hypothesi...

متن کامل

Combining Outputs of Multiple Japanese Named Entity Chunkers by Stacking

In this paper, we propose a method for learning a classifier which combines outputs of more than one Japanese named entity extractors. The proposed combination method belongs to the family of stacked generalizers, which is in principle a technique of combining outputs of several classifiers at the first stage by learning a second stage classifier to combine those outputs at the first stage. Ind...

متن کامل

Improving Optical Music Recognition by Combining Outputs from Multiple Sources

Current software for Optical Music Recognition (OMR) produces outputs with too many errors that render it an unrealistic option for the production of a large corpus of symbolic music files. In this paper, we propose a system which applies image pre-processing techniques to scans of scores and combines the outputs of different commercial OMR programs when applied to images of different scores of...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Systems and Computers in Japan

سال: 2005

ISSN: 0882-1666,1520-684X

DOI: 10.1002/scj.20340