Statistical Distribution Exploration of Tongue Movement for Pathological Articulation on Word/Sentence Level

Pathological articulation exploration, especially the study of the kinematic characteristics of motor organ, is helpful to further reveal the essence of motor dysarthria. Due to the scarcity of the available pathological pronunciation database, there has little research working on the statistic distribution analysis for patients and normal controlled people. This paper applied the distribution method on TORGO database to discover the cognitive and motor rules of dysarthria patients. Single phoneme analysis is effective for locating the specific tongue muscle but ignoring cognitive ability assessment, particularly for the content understanding and the fluency degree of expression by patients. The paper focused on the word/sentence level rather than single phoneme analysis. The reaction time was designed to reveal the relationship between the brain cognition and motor neuron activation. The statistic distribution tells that the cerebral palsy or amyotrophic lateral sclerosis does affect people's reflection and make the patients hard to control the tongue muscles effectively, resulting in unstable reaction time. The articulation velocity of patients appears 5mm/s faster than normal people, at 85mm/s, perhaps due to the factors of the word/sentence data and the big proportion of extra large displacement. It illustrates that the tongue moves relatively coherently and fluently once patients active the muscles, but hard to slow down as the muscle control ability decreases. The spatial occupancy was represented by the maximum articulation movement range (MAMR). We adapted the logarithmic normal distribution to find out the significant threshold for the diagnosis of dysarthria with the MAMR exceeding 7mm along left and right direction and the number of abnormal ranges surpassing 10% of total number. Primary test of MAMR, as an articulatory feature, for speech classification was carried out and it achieves 81% accuracy. These explorations convinced us to apply them to the pathological speech recognition task for improvement in future.