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Related Experiment Video

Updated: Jun 26, 2026

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

Published on: August 9, 2024

Adaptive Phoneme State Learning Architecture for Enhanced Speech Recognition Using Backpropagation Neural Network and

Rashmi Siddalingappa1, Deepa S2, Margaret Savitha2

  • 1Computer and Data Science, York St John University, London, England, E14 2BA, UK.

F1000Research
|June 25, 2026
PubMed
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This study introduces an Adaptive Phoneme State Learning (APSL) algorithm to enhance automated speech recognition (ASR) systems. The APSL-BPNN-HMM model significantly improves accuracy and reduces Word Error Rate (WER) in speech-to-text conversion.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Signal Processing

Background:

  • Automated speech recognition (ASR) systems struggle with accent variability, noise, and data privacy.
  • Existing ASR architectures require enhancements for robust performance across diverse conditions.

Purpose of the Study:

  • To propose and evaluate an enhanced ASR architecture using an Adaptive Phoneme State Learning (APSL) algorithm.
  • To improve phoneme transition modeling and alignment for more accurate speech-to-text conversion.

Main Methods:

  • Developed an APSL algorithm integrated with Backpropagation Neural Network (BPNN) and Hidden Markov Model (HMM).
  • Implemented a multi-stage ASR pipeline including noise reduction, speech-pause detection, and feature extraction.
  • Conducted comparative evaluations using custom and benchmark datasets (BNC, ANC, COCA, Buckeye, Emu).
Keywords:
acoustic modelingback propagation neural networkshidden markov modelspeech recognitionvoice activity detection

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Last Updated: Jun 26, 2026

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

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Published on: August 9, 2024

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Main Results:

  • The APSL-BPNN-HMM model achieved 95.7% recall, 92.95% precision, and 94.53% F-score.
  • Demonstrated significantly lower Word Error Rate (WER) compared to baseline HMM and BPNN systems.
  • Achieved 96% overall accuracy, validating the effectiveness of adaptive learning in ASR.

Conclusions:

  • Adaptive learning within probabilistic frameworks enhances ASR robustness and accuracy.
  • The proposed APSL-BPNN-HMM model offers a significant advancement in speech recognition technology.
  • This approach effectively addresses challenges like accent variability and noise in ASR systems.