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논문검색

The Korean Large Vocabulary Continuous Speech Recognition Platform

초록

영어

For educational and research purposes, we design and evaluate a Korean speech recognition
platform to build a decoder. The platform has an object‐oriented architecture so that researchers
can modify the platform easily and evaluate the performance of a recognition algorithm of their
interests. The platform has the following functionalities: Noise reduction, speech detection,
feature extraction, hidden Markov model (HMM)‐based acoustic modeling, cross‐word modeling, ngram language modeling, n‐best search, word graph generation, and Korean‐specific language
processing. The platform can handle both lexical search trees for large vocabulary speech
recognition, and finite‐state networks for small‐tomedium vocabulary speech recognition. It
performs the word‐dependent n‐best search algorithm with a bigram language model in the
first forward search stage, then extracts a word lattice, and finally rescores the lattice with a
trigram language model in the second backward search stage. In a large vocabulary continuous
speech recognition task, we compare the performance of the platform with HTK and Julius.

목차

Abstract
 1. Introduction
 2. ECHOS
  2.1 Features
  2.2 Structure
  2.3 Functionalities
  2.4 S/W architecture
  2.5 EAPI
 3. Search Algorithm
  3.1 Lexical tree search
  3.2 Two‐pass search
 4. Performance Evaluation
 References

저자정보

  • Oh‐Wook Kwon School of Electrical & Computer Engineering, Chungbuk National University
  • Sukbong Kwon School of Engineering, ICU
  • Sungrack Yun Department of Electrical Engineering, KAIST
  • Gyucheol Jang Department of Electrical Engineering, KAIST
  • Yong‐Rae Kim School of Electrical & Computer Engineering, Chungbuk National University
  • Bong‐Wan Kim SITEC, Wonkwang University
  • Hoirin Kim School of Engineering, ICU
  • Changdong Yoo Department of Electrical Engineering, KAIST
  • Yong‐Ju Lee SITEC, Wonkwang University

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