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Related Experiment Video

Updated: Aug 27, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Improving ultrasound-based multimodal speech recognition with predictive features from representation learning.

Hongcui Wang1, Pierre Roussel2, Bruce Denby2

  • 1Zhejiang University of Water Resources and Electric Power, Hangzhou, China.

JASA Express Letters
|September 26, 2022
PubMed
Summary
This summary is machine-generated.

Predictive representation learning using ultrasound tongue imaging significantly improves speech recognition accuracy. This method outperforms traditional techniques by generating better features for acoustic models, especially when focusing on tongue movements.

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Area of Science:

  • Speech Technology
  • Machine Learning
  • Biomedical Imaging

Background:

  • Representation learning aims to extract high-level features from temporal data.
  • Accurate speech recognition relies on effective feature extraction from articulatory signals.
  • Previous methods often struggle to capture complex dynamics in speech production.

Purpose of the Study:

  • To evaluate the effectiveness of representation learning for speech recognition using articulatory data.
  • To compare predictive features from a 3D CNN with traditional methods like auto-encoders and DCT.
  • To assess the impact of combining tongue and lip imaging modalities on speech recognition performance.

Main Methods:

  • A 3D convolutional neural network (CNN) was trained to predict future frames from ultrasound tongue and optical lip images.
  • Extracted features were used in a continuous hidden Markov model (HMM) based speech recognition system.
  • Performance was evaluated using word error rates (WER) against baseline methods and different acoustic models.

Main Results:

  • Predictive tongue features significantly reduced word error rates compared to auto-encoder or DCT features.
  • Improvements were observed for both Gaussian mixture model (GMM) and deep neural network (DNN) acoustic models.
  • Combining tongue and lip modalities showed a reduced advantage for predictive features compared to single-modality use.

Conclusions:

  • Representation learning, particularly using predictive features from ultrasound tongue imaging, enhances speech recognition.
  • Predictive features offer a superior alternative to traditional feature extraction methods for articulatory speech data.
  • Future research should explore optimal fusion strategies for multi-modal articulatory data in speech recognition.