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Automatic Evaluation of Speech Rhythm Instability and Acceleration in Dysarthrias Associated with Basal Ganglia Dysfunction.

Rusz J, Hlavnička J, Čmejla R, Růžička E - Front Bioeng Biotechnol (2015)

Bottom Line: Although not significant, a tendency for pace acceleration was observed also in the PSP and MSA groups.Our findings underline the crucial role of the basal ganglia in the execution and maintenance of automatic speech motor sequences.We envisage the current approach to become the first step toward the development of acoustic technologies allowing automated assessment of rhythm in dysarthrias.

View Article: PubMed Central - PubMed

Affiliation: Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague , Prague , Czech Republic ; Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague , Prague , Czech Republic.

ABSTRACT
Speech rhythm abnormalities are commonly present in patients with different neurodegenerative disorders. These alterations are hypothesized to be a consequence of disruption to the basal ganglia circuitry involving dysfunction of motor planning, programing, and execution, which can be detected by a syllable repetition paradigm. Therefore, the aim of the present study was to design a robust signal processing technique that allows the automatic detection of spectrally distinctive nuclei of syllable vocalizations and to determine speech features that represent rhythm instability (RI) and rhythm acceleration (RA). A further aim was to elucidate specific patterns of dysrhythmia across various neurodegenerative disorders that share disruption of basal ganglia function. Speech samples based on repetition of the syllable /pa/ at a self-determined steady pace were acquired from 109 subjects, including 22 with Parkinson's disease (PD), 11 progressive supranuclear palsy (PSP), 9 multiple system atrophy (MSA), 24 ephedrone-induced parkinsonism (EP), 20 Huntington's disease (HD), and 23 healthy controls. Subsequently, an algorithm for the automatic detection of syllables as well as features representing RI and RA were designed. The proposed detection algorithm was able to correctly identify syllables and remove erroneous detections due to excessive inspiration and non-speech sounds with a very high accuracy of 99.6%. Instability of vocal pace performance was observed in PSP, MSA, EP, and HD groups. Significantly increased pace acceleration was observed only in the PD group. Although not significant, a tendency for pace acceleration was observed also in the PSP and MSA groups. Our findings underline the crucial role of the basal ganglia in the execution and maintenance of automatic speech motor sequences. We envisage the current approach to become the first step toward the development of acoustic technologies allowing automated assessment of rhythm in dysarthrias.

No MeSH data available.


Related in: MedlinePlus

Example of oscillographic sound pressure signals (up) and their respective spectrograms (down) of the repetition of syllable /pa/ in a healthy (left; RA = −0.6 ms/s, RI = 4%) and dysarthric speaker (right; RA = 73.6 ms/s, RI = 24%). “Pa” represents the syllable /pa/ whereas “R” depicts excessive inspirations due to respiratory problems, and arrows show detected time labels.
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Figure 1: Example of oscillographic sound pressure signals (up) and their respective spectrograms (down) of the repetition of syllable /pa/ in a healthy (left; RA = −0.6 ms/s, RI = 4%) and dysarthric speaker (right; RA = 73.6 ms/s, RI = 24%). “Pa” represents the syllable /pa/ whereas “R” depicts excessive inspirations due to respiratory problems, and arrows show detected time labels.

Mentions: Dysarthric speech typically manifests unstable loudness of voice, imprecise syllable separation, and higher noise levels in occlusions and respirations, making the detection of syllables in the rhythm test difficult. However, the precise identification of syllables requires detection sensitive to imprecisely articulated syllables but insensitive to voiced or noised gaps and inspirations between syllables at the same time (Figure 1). The proposed method overcomes these contradictions in two steps. The first step consists of sensitive syllable detection based on adaptive recognition. The second step determines and removes error detections that are mainly caused by respirations (mostly audible inspirations) and non-speech sounds (mostly turbulent airflow of incomplete occlusion and tongue clicks). Respirations differed from non-speech sounds by prolongation between syllables and a distinctive spectral envelope with formant frequencies above 1 kHz and durations typically longer than 100 ms. Figure 2A shows the main principle of the algorithm whereas Figure 3 highlights the overall decision process overlaid on acoustic input.


Automatic Evaluation of Speech Rhythm Instability and Acceleration in Dysarthrias Associated with Basal Ganglia Dysfunction.

Rusz J, Hlavnička J, Čmejla R, Růžička E - Front Bioeng Biotechnol (2015)

Example of oscillographic sound pressure signals (up) and their respective spectrograms (down) of the repetition of syllable /pa/ in a healthy (left; RA = −0.6 ms/s, RI = 4%) and dysarthric speaker (right; RA = 73.6 ms/s, RI = 24%). “Pa” represents the syllable /pa/ whereas “R” depicts excessive inspirations due to respiratory problems, and arrows show detected time labels.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4513571&req=5

Figure 1: Example of oscillographic sound pressure signals (up) and their respective spectrograms (down) of the repetition of syllable /pa/ in a healthy (left; RA = −0.6 ms/s, RI = 4%) and dysarthric speaker (right; RA = 73.6 ms/s, RI = 24%). “Pa” represents the syllable /pa/ whereas “R” depicts excessive inspirations due to respiratory problems, and arrows show detected time labels.
Mentions: Dysarthric speech typically manifests unstable loudness of voice, imprecise syllable separation, and higher noise levels in occlusions and respirations, making the detection of syllables in the rhythm test difficult. However, the precise identification of syllables requires detection sensitive to imprecisely articulated syllables but insensitive to voiced or noised gaps and inspirations between syllables at the same time (Figure 1). The proposed method overcomes these contradictions in two steps. The first step consists of sensitive syllable detection based on adaptive recognition. The second step determines and removes error detections that are mainly caused by respirations (mostly audible inspirations) and non-speech sounds (mostly turbulent airflow of incomplete occlusion and tongue clicks). Respirations differed from non-speech sounds by prolongation between syllables and a distinctive spectral envelope with formant frequencies above 1 kHz and durations typically longer than 100 ms. Figure 2A shows the main principle of the algorithm whereas Figure 3 highlights the overall decision process overlaid on acoustic input.

Bottom Line: Although not significant, a tendency for pace acceleration was observed also in the PSP and MSA groups.Our findings underline the crucial role of the basal ganglia in the execution and maintenance of automatic speech motor sequences.We envisage the current approach to become the first step toward the development of acoustic technologies allowing automated assessment of rhythm in dysarthrias.

View Article: PubMed Central - PubMed

Affiliation: Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague , Prague , Czech Republic ; Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague , Prague , Czech Republic.

ABSTRACT
Speech rhythm abnormalities are commonly present in patients with different neurodegenerative disorders. These alterations are hypothesized to be a consequence of disruption to the basal ganglia circuitry involving dysfunction of motor planning, programing, and execution, which can be detected by a syllable repetition paradigm. Therefore, the aim of the present study was to design a robust signal processing technique that allows the automatic detection of spectrally distinctive nuclei of syllable vocalizations and to determine speech features that represent rhythm instability (RI) and rhythm acceleration (RA). A further aim was to elucidate specific patterns of dysrhythmia across various neurodegenerative disorders that share disruption of basal ganglia function. Speech samples based on repetition of the syllable /pa/ at a self-determined steady pace were acquired from 109 subjects, including 22 with Parkinson's disease (PD), 11 progressive supranuclear palsy (PSP), 9 multiple system atrophy (MSA), 24 ephedrone-induced parkinsonism (EP), 20 Huntington's disease (HD), and 23 healthy controls. Subsequently, an algorithm for the automatic detection of syllables as well as features representing RI and RA were designed. The proposed detection algorithm was able to correctly identify syllables and remove erroneous detections due to excessive inspiration and non-speech sounds with a very high accuracy of 99.6%. Instability of vocal pace performance was observed in PSP, MSA, EP, and HD groups. Significantly increased pace acceleration was observed only in the PD group. Although not significant, a tendency for pace acceleration was observed also in the PSP and MSA groups. Our findings underline the crucial role of the basal ganglia in the execution and maintenance of automatic speech motor sequences. We envisage the current approach to become the first step toward the development of acoustic technologies allowing automated assessment of rhythm in dysarthrias.

No MeSH data available.


Related in: MedlinePlus