Speech Recognition Using Randomized Relational Decision Trees.

Abstract

We explore the possibility of recognizing speech signals using a large collection of coarse acoustic events, which describe temporal relations between a small number of local features of the spectrogram. The major issue of invariance to changes in duration of speech signal events is addressed by defining temporal relations in a rather coarse manner, allowing for a large degree of slack. The approach is greedy in that it does not offer an "explanation" of the entire signal as the Hidden Markov Models (HMMs) approach does; rather it accesses small amounts of relational information to determine a speech unit or class. This implies that we recognize words as units, without recognizing their subcomponents. Multiple randomized decision trees are used to access the large pool of acoustic events in a systematic manner and are aggregated to produce the classifier.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 23, 1999
Accession Number
ADA364818

Entities

People

  • Alejandro Murua
  • Yali Amit

Organizations

  • University of Chicago

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Acoustic Signals
  • Automated Speech Recognition
  • Data Sets
  • Frequency
  • Hidden Markov Models
  • Identification
  • Information Science
  • Machine Learning
  • Neural Networks
  • Probability
  • Probability Distributions
  • Recognition
  • Speech
  • Statistical Analysis
  • Statistics
  • Time Intervals

Readers

  • Parallel and Distributed Computing.
  • Speech Processing/Speech Recognition.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms