Self Organizing Maps as the Perceptual Acquisition Model

Bibliographic Information

Other Title
  • 知識獲得モデルとしての自己組織化マップ
  • -Unsupervised Phoneme Learning from Continuous Speech-
  • -連続音声からの教師なし音素体系の学習-

Abstract

We assume that SOM is adequate as a language acquisition model of the native phonetic system. However, many studies don't consider the quantitative features (the appearance frequency and the number of frames of each phoneme) of the input data. Our model is designed to learn values of the acoustic characteristic of a natural continuous speech and to estimate the number and boundaries of the vowel categories without using explicit instructions. In the simulation trial, we investigate the relationship between the quantity of learning and the accuracy for the vowels in a single Japanese speaker's natural speech. As a result, it is found that the recognition accuracy rate (of our model) are 5% (/u/)-92% (/s/).

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Details 詳細情報について

  • CRID
    1390001205187701376
  • NII Article ID
    130003393774
  • DOI
    10.3156/jsoft.26.510
  • ISSN
    18817203
    13477986
  • Text Lang
    ja
  • Data Source
    • JaLC
    • Crossref
    • CiNii Articles
    • KAKEN
  • Abstract License Flag
    Disallowed

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