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Recent dysarthric speech recognition studies using mixed data from a collection of neurological diseases suggested articulatory data can help to improve the speech recognition performance. This project was specifically designed for the speakerindependent recognition of dysarthric speech due to amyotrophic lateral sclerosis (ALS) using articulatory data. In this paper, we investigated three across-speaker normalization approaches in acoustic, articulatory, and both spaces: Procrustes matching (a physiological approach in articulatory space), vocal tract length normalization (a data-driven approach in acoustic space), and feature space maximum likelihood linear regression (a model-based approach for both spaces), to address the issue of high degree of variation of articulation across different speakers. A preliminary ALS data set was collected and used to evaluate the approaches. Two recognizers, Gaussian mixture model (GMM) -hidden Markov model (HMM) and deep neural network (DNN) -HMM, were used. Experimental results showed adding articulatory data significantly reduced the phoneme error rates (PERs) using any or combined normalization approaches. DNN-HMM outperformed GMM-HMM in all configurations. The best performance (30.7% PER) was obtained by triphone DNN-HMM + acoustic and articulatory data + all three normalization approaches, a 15.3% absolute PER reduction from the baseline using triphone GMM-HMM + acoustic data.

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Articulatory data have gained increasing interest in speech recognition with or without acoustic data. Electromagnetic articulograph (EMA) is one of the affordable, currently used techniques for tracking the movement of flesh points on articulators (e.g., tongue) during speech. Determining an optimal set of sensors is important for optimizing the clinical applications of EMA data, due to the inconvenience of attaching sensors on tongue and other intraoral articulators, particularly for patients with neurological diseases. A recent study found an optimal set (tongue tip and body back, upper and lower lips) on tongue and lips for isolated phoneme, word, or short phrase classification from articulatory movement data. This four-sensor set, however, has not been verified in continuous silent speech recognition. In this paper, we investigated the use of data from sensor combinations in continuous speech recognition to verify the finding using a publicly available data set MOCHA-TIMIT. The long-standing speech recognition approach Gaussian mixture model (GMM)-hidden Markov model (HMM) and a recently available approach deep neural network (DNN)-HMM were used as the recognizers. Experimental results confirmed that the four-sensor set is optimal out of the full set of sensors on tongue, lips, and jaw. Adding upper incisor and/or velum data further improved the recognition performance slightly.