Abstract

State-of-the-art Automatic Speech Recognition (ASR) systems typically use phoneme as the subword units. In this paper, we investigate a system where the word models are defined in-terms of two different subword units, i.e., phonemes and graphemes. We train models for both the subword units, and then perform decoding using either both or just one subword unit. We have studied this system for American English language where there is weak correspondence between the grapheme and phoneme. The results from our studies show that there is good potential in using grapheme as auxiliary subword units.

Keywords:
Grapheme Computer science Speech recognition Hidden Markov model Decoding methods Artificial intelligence Word (group theory) Natural language processing State (computer science) Linguistics Programming language Algorithm

Metrics

19
Cited By
2.32
FWCI (Field Weighted Citation Impact)
16
Refs
0.89
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Speech Recognition and Synthesis
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and dialogue systems
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
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