A Multi-Resolution Hidden Markov Model Using Class-Specific Features

Abstract

We address the problem in signal classification applications, such as automatic speech recognition (ASR) systems that employ the hidden Markov model (HMM), that it is necessary to settle for a fixed analysis window size and a fixed feature set. This is despite the fact that complex signals such as human speech typically contain a wide range of signal types and durations. We apply the probability density function (PDF) projection theorem to generalize the hidden Markov model (HMM) to utilize a different features and segment length for each state. We demonstrate the algorithm using speech analysis so that long-duration phonemes such as vowels and short-duration phonemes such as plosives can utilize feature extraction tailored to their own time scale.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2008
Accession Number
ADA494596

Entities

People

  • Paul Baggenstoss

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Automated Speech Recognition
  • Data Analysis
  • Data Science
  • Frequency
  • Frequency Response
  • Hidden Markov Models
  • Information Science
  • Markov Models
  • Models
  • Noise
  • Probability
  • Recognition
  • Sequences
  • Speech Analysis
  • Standards

Fields of Study

  • Computer science
  • Engineering

Readers

  • Maritime Combat Support and Expeditionary Logistics.
  • Speech Processing/Speech Recognition.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation