# Articulatory information and Multiview Features for Large Vocabulary Continuous Speech Recognition

arXiv: https://arxiv.org/abs/1802.05853 · HTML: https://mlsanity.com/paper/1802.05853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 4 of 4)

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- [Enhancing Speech Articulation Analysis using a Geometric Transformation of the X-ray Microbeam Dataset](https://mlsanity.com/paper/2305.10775/) (2023)
- [Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions](https://mlsanity.com/paper/2503.22711/) (2025)
- [Exploring Dynamic Parameters for Vietnamese Gender-Independent ASR](https://mlsanity.com/paper/2507.22964/) (2025)

## Abstract (arXiv)

This paper explores the use of multi-view features and their discriminative transforms in a convolutional deep neural network (CNN) architecture for a continuous large vocabulary speech recognition task. Mel-filterbank energies and perceptually motivated forced damped oscillator coefficient (DOC) features are used after feature-space maximum-likelihood linear regression (fMLLR) transforms, which are combined and fed as a multi-view feature to a single CNN acoustic model. Use of multi-view feature representation demonstrated significant reduction in word error rates (WERs) compared to the use of individual features by themselves. In addition, when articulatory information was used as an additional input to a fused deep neural network (DNN) and CNN acoustic model, it was found to demonstrate further reduction in WER for the Switchboard subset and the CallHome subset (containing partly non-native accented speech) of the NIST 2000 conversational telephone speech test set, reducing the error rate by 12% relative to the baseline in both cases. This work shows that multi-view features in association with articulatory information can improve speech recognition robustness to spontaneous and non-native speech.