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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Related Experiment Video

Updated: May 29, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Parallel algorithms for syllable recognition in continuous speech.

R De Mori1, P Laface, Y Mong

  • 1Department of Computer Science, Concordia University, Montreal, P. Q. H3G 1M8, Canada.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a distributed rule-based system for automatic speech recognition. The system effectively segments and recognizes phrases using cooperative expert programs executing operator sequences.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Speech Processing

Background:

  • Automatic speech recognition (ASR) systems require robust methods for acoustic analysis and feature extraction.
  • Traditional ASR approaches often face challenges with complex acoustic environments and diverse speech patterns.

Purpose of the Study:

  • To present a novel distributed rule-based system for automatic speech recognition.
  • To detail the methodology for acoustic property extraction and feature hypothesization within this system.

Main Methods:

  • A distributed rule-based system architecture is employed.
  • Sequences of operators, termed 'plans,' are utilized for acoustic property extraction and feature hypothesization.
  • Cooperative expert programs execute these plans to process speech data.

Main Results:

  • The system demonstrates successful automatic segmentation of speech.
  • The system achieves accurate recognition of phrases composed of connected letters and digits.
  • Experimental results validate the system's performance in phrase recognition tasks.

Conclusions:

  • The described distributed rule-based system offers an effective approach to automatic speech recognition.
  • The methodology of using operator sequences executed by cooperative experts is viable for ASR.
  • Further research can explore system scalability and performance on more complex speech tasks.