Automated voice pathology discrimination from audio recordings benefits from phonetic analysis of continuous speech

In this paper we evaluate the hypothesis that automated methods for diagnosis of voice disorders from speech recordings would benefit from contextual information found in continuous speech. Rather than basing a diagnosis on how disorders affect the average acoustic properties of the speech signal, the idea is to exploit the possibility that different disorders will cause different acoustic changes within different phonetic contexts. Any differences in the pattern of effects across contexts would then provide additional information for discrimination of pathologies. We evaluate this approach using two complementary studies: the first uses a short phrase which is automatically annotated using a phonetic transcription, the second uses a long reading passage which is automatically annotated from text. The first study uses a single sentence recorded from 597 speakers in the Saarbrucken Voice Database to discriminate structural from neurogenic disorders. The results show that discrimination performance for these broad pathology classes improves from 59% to 67% unweighted average recall when classifiers are trained for each phone-label and the results fused. Although the phonetic contexts improved discrimination, the overall sensitivity and specificity of the method seems insufficient for clinical application. We hypothesise that this is because of the limited contexts in the speech audio and the heterogeneous nature of the disorders. In the second study we address these issues by processing recordings of a long reading passage obtained from clinical recordings of 60 speakers with either Spasmodic Dysphonia or Vocal fold Paralysis. We show that discrimination performance increases from 80% to 87% unweighted average recall if classifiers are trained for each phone-labelled region and predictions fused. We also show that the sensitivity and specificity of a diagnostic test with this performance is similar to other diagnostic procedures in clinical use. In conclusion, the studies confirm that the exploitation of contextual differences in the way disorders affect speech improves automated diagnostic performance, and that automated methods for phonetic annotation of reading passages are robust enough to extract useful diagnostic information

Objectively measuring the effects of sleep on reading comprehension and sustained selective attention

The overall performance of a university is measured by retention rates of students. Because individuals who achieve lower grade point averages are at a higher risk of failing or dropping out of college, the academic performance of undergraduates should be the target of concern to maintain good retention rates. Academic performance, which is associated with attention and reading comprehension abilities, is affected by the sleep behavior of students. In regards to college students and sleep, research has indicated that college students demonstrate habitually poor sleep habits. Poor sleep habits have been linked to impaired attention and concentration abilities, but the measures used to quantify these associations have relied on self-report. Additionally, the effects of sleep and cognitive functioning has focused on clinical populations that meet the requirements of either a sleep disorder or Attention Deficit Hyperactivity Disorder, with limited research examining the effects of sleep behavior on a non-clinical population. The purpose of this study was to test the objective measures of sleep behavior and cognitive functioning with healthy young adults enrolled in an undergraduate university. Specifically, this study focused on the role that the different stages of sleep play in cognitive functioning in addition to the roles of one\u27s sleep quality and sleep quantity. Collected data were analyzed using one-way analysis of variance as well as multiple regression analyses. Results demonstrated that students\u27 sleep architecture (i.e., sleep quantity, time spent in different sleep stages), daytime sleepiness, or sleep quality did not significantly impact sustained attention or reading comprehension; however, computation of effect sizes revealed a strong effect for sleep quantity and reading comprehension

The effects of reading and self-graphing on the reading fluency and comprehension of third grade students with special needs

The purpose of the study was to examine the effects of rereading and self-graphing on fluency and comprehension of third grade students with special needs. This study implemented a pretest-posttest design. The participants were six students with special needs from a third grade inclusion classroom reading at least two grade levels below third grade. Data was collected during a baseline phase, intervention phase, and postintervention phase. The independent variables were the use of rereading and selfgraphing of passages from the Critical Reading Inventory and The Jerry Johns Basic Reading Inventory. The dependent variable was the measure of the participants\u27 reading fluency and comprehension using the Critical Reading Inventory and The Jerry Johns Basic Reading Inventory. Overall, the results of the study demonstrated reading and selfgraphing to be an effective intervention to increase students\u27 fluency and comprehension. Participants in the study showed an increase in both fluency and comprehension by rereading and self-graphing results. Three participants had a greater increase in fluency, than in comprehension. Three of the participants had a greater increase in comprehension than in fluency. Results of this study show that rereading and self-monitoring of progress can be an effective strategy to improve the reading fluency and comprehension for students with special needs

The cerebral basis for language learner strategies: A near-infrared spectroscopy study

In this paper, we validate Macaro\u27s (2006) model of strategy use among language learners by assessing the amount of neural activity around the prefrontal cortex, the supposed locus of working memory (WM). We also examine whether WM activation during first language (L1) strategy deployment is lower than WM activation during second language (L2) strategy deployment, as predicted by Macaro\u27s model. In theanalysis, we consider data obtained through an innovative neuroimaging technique (nearinfrared spectroscopy) and stimulated-recall interviews. The results reveal greater brain activity during execution of the L1 and L2 tasks than in a control condition; further, use of strategies in the L2 resulted in stronger WM activation than use of strategies in the L1. These results provide partial support for the validity of Macaro\u27s model

The cerebral basis for language learner strategies: A near-infrared spectroscopy study

In this paper, we validate Macaro’s (2006) model of strategy use among language learners by assessing the amount of neural activity around the prefrontal cortex, the supposed locus of working memory (WM). We also examine whether WM activation during first language (L1) strategy deployment is lower than WM activation during second language (L2) strategy deployment, as predicted by Macaro’s model. In the analysis, we consider data obtained through an innovative neuroimaging technique (near-infrared spectroscopy) and stimulated- recall interviews. The results reveal greater brain activity during execution of the L1 and L2 tasks than in a control condition; further, use of strategies in the L2 resulted in stronger WM activation than use of strategies in the L1. These results provide partial support for the validity of Macaro’s model