Abnormal Pauses Research Articles

The combination of accent method and phonemic contrast: an innovative strategy to improve speech production on post-stroke dysarthria.

Speech production includes segmental and suprasegmental features, which interact and cooperate with each other. Impaired speech production is common in individuals with post-stroke dysarthria. The commonly used phonemic contrast therapy and Accent Method in clinical practice can improve the segmental and suprasegmental aspects, respectively. This study aimed to explore the feasibility and immediate effectiveness of the combination of specific rhythm patterns of the Accent Method and phonemic contrast on speech production. Fifteen poststroke dysarthria patients (12 males and three females) first received accentuation task of three rhythm patterns (Largo-slow, Andante-medium, Allegro-fast) and later received speech task in appropriate rhythm patterns combined with phonemic contrast materials and non-phonemic contrast materials. Speech parameters were analyzed by considering speech clarity and prosody. The results showed that the number of correct target syllables, sentence clarity, and standard deviation of intensity increased significantly, and the average length of pauses and abnormal pause times decreased significantly in Andante (medium) compared to other rhythms. The number of correct target syllables, sentence clarity, and standard deviation of intensity increased significantly compared with those in the non-phonemic contrast in Andante (medium). The combination of phonemic contrast and Accent Method was verified to have an immediate effect on speech production in Mandarin speakers with post-stroke dysarthria and could be further validated in other diseases with impaired speech production in the clinic in the future.

Nonword reading by adults who stutter in a transparent orthography

PurposeUsing word- and nonword-reading passages in Kannada, which has a transparent orthography, we attempted to determine (a) whether orthographic differences between English and Kannada may explain the observed differences in stutter rates on nonwords, and (b) whether longer nonwords, like words, incur higher rates of stutters. MethodsStutters are defined as sound or syllable repetitions, sound prolongations, broken words or nonwords (a pause within a word or nonword), abnormal pauses, and intrusive vowel-like sounds. Twenty-six persons, who stutter, read the word and nonword passages. The nonwords were created by changing the first syllable of each word; otherwise words and nonwords were equivalent in length and syllable structure. Stutters were counted from audio-recordings and statistically analyzed. ResultsPWS stuttered on words in varying amounts and in significantly larger amounts on nonwords. Stutter frequency increased roughly in proportion to the increase in the length of phonological words (previously known) and nonwords (reported for the first time here). ConclusionThe results cannot be attributed to the difficulty of pronouncing nonwords because Kannada orthography has a one-to-one relationship between the written and spoken forms of words. Speech production is a multi-stage process consisting of ideation, lemma selection, phonological word creation, and the articulatory planning and execution. Because nonwords lack meaning and clearly identifiable part of speech, it appears that stutters arise late in the speech production process at the phonological word formation and articulatory planning stages. Meaning, lexicality, and morphosyntax may not contribute significantly to the occurrence of stutters.

SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches.

The abnormal pause or rate reduction in breathing is known as the sleep-apnea hypopnea syndrome and affects the quality of sleep of an individual. A novel method for the detection of sleep apnea events (pause in breathing) from peripheral oxygen saturation (SpO2) signals obtained from wearable devices is discussed in this paper. The paper details an apnea detection algorithm of a very high resolution on a per-second basis for which a 1-dimensional convolutional neural network- which we termed SomnNET- is developed. This network exhibits an accuracy of 97.08% and outperforms several lower resolution state-of-the-art apnea detection methods. The feasibility of model pruning and binarization to reduce the computational complexity is explored. The pruned network with 80% sparsity exhibited an accuracy of 89.75%, and the binarized network exhibited an accuracy of 68.22%. The performance of the proposed networks is compared against several state-of-the-art algorithms.

The Short-term Mass Change of Greenland Ice Sheet and the Atmospheric Forcing

In recent decades, the global warming drives the huge ice sheet melting in northern hemisphere. Previous studies on long-term melting rates of Greenland have basically reached agreement, but there is still a lack of the knowledge of the pattern the short-term ice mass change, makes it difficult to fulfill the understanding of the mechanism of the ice sheet. In this study, we used Gravity Recovery and Climate Experiment (GRACE) RL06 data to derive the time series of mass variation of Greenland Ice Sheet (GrIS), from January 2003 to December 2015. We derived the short-term mass change rate during 2003-2015 and found that the GrIS has experienced four melting stages, i.e., a steady melting (-204.4 Gt/yr) phase form 2003 to 2009, an abrupt accelerating (-384.6 Gt/yr) phase form 2010 to 2012, an abnormal pause (+24.8 Gt/yr) year in 2013 and a recovering (-207.3 Gt/yr) phase from 2014 to 2015. In addition, using ensemble empirical mode decomposition (EEMD), the high frequency signal has been removed and the annual mass change has been studied, which shows inter-annual variability. Meanwhile, we correlated the annual mass change with the Greenland Blocking Index (GBI) and North Atlantic Oscillation (NAO) during summertime and the correlation for 2003-2015 was found at r=-0.63 between the summer GBI and the annual mass change, while r=0.57 between summer NAO and the annual mass change. Our results indicate that the short-term mass change of GrIS mainly forced by atmospheric variability.

Spectro-Temporal Sparsity Characterization for Dysarthric Speech Detection

To assist the clinical diagnosis and treatment of neurological diseases that cause speech dysarthria such as Parkinson's disease (PD), it is of paramount importance to craft robust features which can be used to automatically discriminate between healthy and dysarthric speech. Since dysarthric speech of patients suffering from PD is breathy, semi-whispery, and is characterized by abnormal pauses and imprecise articulation, it can be expected that its spectro-temporal sparsity differs from the spectro-temporal sparsity of healthy speech. While we have recently successfully used temporal sparsity characterization for dysarthric speech detection, characterizing spectral sparsity poses the challenge of constructing a valid feature vector from signals with a different number of unaligned time frames. Further, although several non-parametric and parametric measures of sparsity exist, it is unknown which sparsity measure yields the best performance in the context of dysarthric speech detection. The objective of this paper is to demonstrate the advantages of spectro-temporal sparsity characterization for automatic dysarthric speech detection. To this end, we first provide a numerical analysis of the suitability of different non-parametric and parametric measures (i.e., $l_1$ -norm, kurtosis, Shannon entropy, Gini index, shape parameter of a Chi distribution, and shape parameter of a Weibull distribution) for sparsity characterization. It is shown that kurtosis, the Gini index, and the parametric sparsity measures are advantageous sparsity measures, whereas the $l_1$ -norm and entropy measures fail to robustly characterize the temporal sparsity of signals with a different number of time frames. Second, we propose to characterize the spectral sparsity of an utterance by initially time-aligning it to the same utterance uttered by a (arbitrarily selected) reference speaker using dynamic time warping. Experimental results on a Spanish database of healthy and dysarthric speech show that estimating the spectro-temporal sparsity using the Gini index or the parametric sparsity measures and using it as a feature in a support vector machine results in a high classification accuracy of 83.3%.

Cluttering symptoms in school-age children by communicative context: A preliminary investigation

Purpose: The objective of this study was to compare the symptoms of cluttering among school-age children who do and do not clutter in the contexts of monologue, conversation and expository discourse.Method: A matched pairs design was used to compare cluttering symptoms according to the Lowest Common Denominator (LCD) definition of cluttering, a definition representing the core speech and fluency characteristics of cluttering agreed upon among experts. Cluttering symptoms (over-coarticulated words, normal disfluencies, abnormal pauses) in eight school-aged males with cluttering were compared to eight controls matched by sex and grade level in school. Symptoms were compared in the speech contexts of conversation, monologue and expository discourse.Result: Regardless of the speaking context, significantly more over-coarticulated words were found in children with cluttering (CWC) as compared to controls. Significantly more normal disfluencies were produced by CWC during monologue only.Conclusion: Study findings confirm increased over-coarticulation and normal disfluencies in specific speaking contexts in CWC when compared to controls. These findings provide the premise for clinical implications for cluttering assessment and diagnosis. Findings also provide the basis for further investigation of the validity of the LCD’s symptom of abnormal pausing for accurate diagnosis of people who clutter.

Automatic detection of sleep apnea events based on inter-band energy ratio obtained from multi-band EEG signal.

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.

A comparison between native and non-native speech for automatic speech recognition

This study investigates differences in sentence and story production between native and non-native speakers of English for use with a system of Automatic Speech Recognition (ASR). Previous studies have shown that production errors by non-native speakers of English include misproduced segments (Flege, 1995), longer pause duration (Anderson-Hsieh and Venkatagiri, 1994), abnormal pause location within clauses (Kang, 2010), and non-reduction of function words (Jang, 2009). The present study uses phonemically balanced sentences from TIMIT (Garofolo et al., 1993) and a story to provide an additional comparison of the differences in production by native and non-native speakers of English. Consistent with previous research, preliminary results suggest that non-native speakers of English fail to produce flaps and reduced vowels, insert or delete segments, engage in more self-correction, and place pauses in different locations from native speakers. Non-native English speakers furthermore produce different patterns of intonation from native speakers and produce errors indicative of transfer from their L1 phonology, such as coda deletion and vowel epenthesis. Native speaker productions also contained errors, the majority of which were content-related. These results indicate that difficulties posed by English ASR systems in recognizing non-native speech are due largely to the heterogeneity of non-native production.

Examination of validity in spoken language evaluations: Adult onset stuttering following mild traumatic brain injury1

Reports of increased incidence of adult onset stuttering in veterans and service members with mild traumatic brain injury (mTBI) from combat operations in Iraq and Afghanistan lead to a reexamination of the neurogenic vs. psychogenic etiology of stuttering. This article proposes to examine the merit of the dichotomy between neurogenic and psychogenic bases of stuttering, including symptom exaggeration, for the evaluation and treatment of the disorder. Two case studies of adult onset stuttering in service members with mTBI from improvised explosive device blasts are presented in detail. Speech fluency was disrupted by abnormal pauses and speech hesitations, brief blocks, rapid repetitions, and occasional prolongations. There was also wide variability in the frequency of stuttering across topics and conversational situations. Treatment focused on reducing the frequency and severity of dysfluencies and included educational, psychological, environmental, and behavioral interventions. Stuttering characteristics as well as the absence of objective neurological findings ruled out neurogenic basis of stuttering in these two cases and pointed to psychogenic causes. However, the differential diagnosis had only limited value for developing the plan of care. The successful outcomes of the treatment serve to illustrate the complex interaction of neurological, psychological, emotional, and environmental factors of post-concussive symptoms and to underscore the notion that there are many facets to symptom presentation in post-combat health.

Mandibular advancement device in sleep apnea: preliminary study and initial results

Background: Sleep apnea is a disorder characterized by abnormal pauses in breathing during sleep due to an alteration in anatomy or function of upper airway. When the airway collapses during sleep, oxygen desaturation and sleep fragmentation appears, leading to sleepiness, tiredness, neuropsiquiatric and cardiometabolic diseases. Prevalence of symptomatic sleep apnea in Spain is 6%. Mandibular advancement device (MAD) changes mandibular and tongue position and increases pharyngeal airway dimensions. It is indicated for snoring and sleep apnea treatment.

Isolated ocular flutter

A previously healthy 30-year-old man presented with gradually worsening oscillopsia 2 weeks after an unusual headache; there was ocular flutter on examination (video on the Neurology ® Web site at [www.neurology.org][1]) without other findings. Brain MRI was unremarkable. CSF showed mild pleocytosis (11 cells/mm3), but no evidence of active viral infections (herpes simplex virus, cytomegalovirus, varicella-zoster virus, Epstein-Barr virus, human herpesvirus 6, and measles). Serum antiganglioside antibodies (including anti-GQ1b antibody) were negative. The symptom resolved without treatment within 4 weeks. Ocular flutter is rare and may be isolated, although it is usually accompanied by generalized myoclonus or truncal ataxia.1,2 Brainstem (omnipause neurons in the paramedian pontine reticular formation3) or cerebellar dysfunction may contribute, as may abnormal pause cell control over saccadic burst neurons. [1]: http://www.neurology.org

Studies show link between sleep apnoea and cancer

Sleep apnoea—a disorder characterised by abnormal pauses in breathing during sleep—has been linked to cancer incidence and mortality, according to results of two new studies, both presented at the American Thoracic Society 2012 conference (May 18–23, San Francisco, CA, USA).

Electrophysiological effects of intravenous rilmenidine in man

Ten patients (44 y), 6 with the Wolff-Parkinson-White syndrome, and none with hypertensive disease, underwent electrophysiological studies before and after intravenous infusion of a single dose of 1 mg rilmenidine administered over 15 min. The regimen produced a mean plasma rilmenidine concentration of 3.16 ng.ml-1 at the end of the infusion. There was no significant change in sinus cycle length, PR interval, QRS, QT duration or in PA, AH and HV intervals. Estimated sinoatrial conduction time and corrected sinus node recovery time did not significantly change. In one patient, however, an abnormal pause was noted after termination of rapid atrial pacing. The right atrial effective refractory period decreased from 209 to 194 ms. There was no significant change in the anterograde and retrograde block cycle length or in the refractoriness of the nodal, ventricular and accessory pathways. The cycle length of induced reciprocating tachycardia decreased slightly from 374 to 351 ms. No patient exhibited an abnormal response to the carotid sinus massage. The findings indicate that intravenous administration of 1 mg rilmenidine exerts modest effects on the electrophysiological parameters of the human heart.