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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning.

Mengzhuo Liu1, Yangjie Wei1

  • 1College of Computer Science and Engineering, Northeastern University, Wenhua Street 3, Shenyang 110819, China.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel speech recognition model combining capsule networks with conformer architectures. The enhanced feature extraction significantly reduces word error rates, improving accuracy without a language model.

Keywords:
Chinese speech recognitionbi-transformercapsule networkconformerend-to-end

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Area of Science:

  • Artificial Intelligence
  • Speech Processing
  • Machine Learning

Background:

  • Conformer models in speech processing suffer from information loss during convolution and pooling, limiting shallow feature extraction.
  • Incomplete feature extraction impacts the accuracy and speed of current speech recognition systems.
  • Existing architectures struggle to fully capture the structural nuances within speech signals.

Purpose of the Study:

  • To enhance the accuracy of speech feature extraction in conformer-based models.
  • To propose a novel end-to-end speech recognition architecture integrating capsule networks.
  • To improve the overall performance and robustness of speech recognition systems.

Main Methods:

  • Introduced a capsule network with dynamic routing into the conformer model to preserve speech structural information.
  • Incorporated a residual network within capsule blocks to enhance mapping ability and reduce training complexity.
  • Utilized a bi-transformer model in the decoding network for improved bidirectional hypothesis consistency.

Main Results:

  • The proposed model achieved a lower word error rate compared to existing models, even without a language model.
  • Demonstrated superior speech feature extraction and learning capabilities due to the capsule network integration.
  • Verified the model's effectiveness and robustness across various experimental setups.

Conclusions:

  • The integration of capsule networks significantly enhances speech feature extraction and learning in conformer-based models.
  • The novel architecture offers improved accuracy and robustness for speech recognition tasks.
  • The model shows potential for broader applications in speech-related technologies.