Abstract
Dysarthria is a traumatic neuromotor disorder that affects the physical production of speech. It reduces the function of primary articulators that are involved in speech. Recent research has presented that the use of articulatory data provide a better assessment towards identifying the speech intelligibility of dysarthric speakers over acoustic models that are modelled based on the biological perception of speech through Mel scale than directly involving the anatomy of speech production. Articulatory data of speech, include positional data of the articulators obtained from 12 Electromagnetic Articulograph (EMA) sensor channels with each sensor channel containing 6 parameters namely x, y, z, Φ, θ, and rms. While identifying the phone intelligibility deficit for the dysarthric speakers, articulatory data of all 12 sensor combinations each with 6 parameters may create turbulence in the intelligibility assessment. Hence, an appropriate mapping of the sound units with the sensor combinations is vital. The current work, focuses on mapping the phones with the EMA sensor channels based on their place of articulation. The parameters are also grouped based on the 3D-spherical co-ordinate distribution of the articulator as (x, y, θ) and (y, z, Φ). The mapping of the EMA sensor channels with the sound units are further validated through HMM-based acoustic only models and FDA scores. The phones are trained through their corresponding articulatory sensor combination information using a 5-class support vector machine and tested through a 10-fold cross validation technique for both the parameter groups. The 5 classes include mild, moderate, moderate-to-severe, severe and normal. Each phone can be classified to any of the 5 class in both the parameter groups based on their speech intelligibility. The results show that the EMA sensor combinations with (y, z, Φ) parameter group for each phone coincide well with the acoustic and FDA scores.
Published Version
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