Analysis of Articulation Errors in Dysarthric Speech.

Imprecise articulation is the major issue reported in various types of dysarthria. Detection of articulation errors can help in diagnosis. The cues derived from both the burst and the formant transitions contribute to the discrimination of place of articulation of stops. It is believed that any acoustic deviations in stops due to articulation error can be analyzed by deriving features around the burst and the voicing onsets. The derived features can be used to discriminate the normal and dysarthric speech. In this work, a method is proposed to differentiate the voiceless stops produced by the normal speakers from the dysarthric by deriving the spectral moments, two-dimensional discrete cosine transform of linear prediction spectrum and Mel frequency cepstral coefficients features. These features and cosine distance based classifier is used for the classification of normal and dysarthic speech.

Acoustic analysis of misarticulated trills in cleft lip and palate children.

In this paper, acoustic analysis of misarticulated trills in cleft lip and palate speakers is carried out using excitation source based features: strength of excitation and fundamental frequency, derived from zero-frequency filtered signal, and vocal tract system features: first formant frequency (F1) and trill frequency, derived from the linear prediction analysis and autocorrelation approach, respectively. These features are found to be statistically significant while discriminating normal from misarticulated trills. Using acoustic features, dynamic time warping based trill misarticulation detection system is demonstrated. The performance of the proposed system in terms of the F1-score is 73.44%, whereas that for conventional Mel-frequency cepstral coefficients is 66.11%.