Characterizing Deletion Transformations across Dialects using a Sophisticated Tying Mechanism

Abstract

We propose a sophisticated tying mechanism for modeling deletion transformations between dialects. We empirically show that the proposed tying mechanism reduces deletion errors by 33% when compared to a baseline system using a standard tying mechanism. Statistical tests show that the proposed and baseline models make statistically different errors, thus suggesting that they are complementary systems in dialect recognition tasks. Pronunciation rules learned by our proposed system quantify the occurrence frequency of known rules, and suggest rule candidates for further linguistic studies.

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Document Details

Document Type
Technical Report
Publication Date
Mar 30, 2011
Accession Number
ADA570549

Entities

People

  • Joseph P. Campbell
  • Nancy F. Chen
  • Wade Shen

Organizations

  • Massachusetts Institute of Technology

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  • C4I

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  • Automated Speech Recognition
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Fields of Study

  • Computer science

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  • Computational Linguistics
  • Theoretical Analysis.