Maximum likelihood training and adaptation of embedded speech recognizers for mobile environments

Youngkyu Cho, Dongsuk Yook

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

Original languageEnglish
Pages (from-to)160-162
Number of pages3
JournalETRI Journal
Volume32
Issue number1
DOIs
Publication statusPublished - 2010 Feb

Keywords

  • Embedded speech recognition
  • Maximum likelihood distribution clustering (MLDC)
  • Quantized HMM

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science(all)
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Maximum likelihood training and adaptation of embedded speech recognizers for mobile environments'. Together they form a unique fingerprint.

Cite this