Effect of voice support level and spectrum on conversational speech

2021 ◽  
Vol 150 (4) ◽  
pp. 2635-2646
Author(s):  
Mary Rapp ◽  
Densil Cabrera ◽  
Manuj Yadav
Keyword(s):  
Conversational Speech ◽  
Support Level

Reliability of Clinician Judgments of Severity of Phonological Impairment

1995 ◽  
Vol 4 (3) ◽  
pp. 39-46 ◽  
Author(s):  
Susan K. Rafaat ◽  
Susan Rvachew ◽  
Rebecca S. C. Russell
Keyword(s):  
Conversational Speech ◽  
Speech Language Pathologists ◽  
Age Appropriate ◽  
Citation Form ◽  
Phonological Abilities ◽  
Preschool Aged Children

Pairs of speech-language pathologists independently rated severity of phonological impairment for 45 preschoolers, aged 30 to 65 months. Children were rated along a continuum from normal to profound. In addition to judging overall severity of impairment, the clinicians provided separate ratings based on citation form and conversational samples. A judgment of intelligibility of conversational speech was also required. Results indicated that interclinician reliability was adequate (80% agreement) for older preschool-aged children (4-1/2 years and above) but that judgments by speechlanguage pathologists were not sufficiently reliable for children under 3-1/2 years of age 40% agreement). Children judged to have age appropriate phonological abilities were not clearly distinguishable from children judged to have a mild delay. Educating speech-language pathologists regarding the normative phonological data that are available with respect to young preschoolers, and ensuring that such data are readily accessible for assessment purposes, is required.

Densely Connected Networks for Conversational Speech Recognition

2018 ◽  
Author(s):  
Kyu Han ◽  
Akshay Chandrashekaran ◽  
Jungsuk Kim ◽  
Ian Lane
Keyword(s):  
Speech Recognition ◽  
Conversational Speech

The Association Among Social Support, Self-Efficacy, Use of Mobile Apps, and Physical Activity: Structural Equation Models With Mediating Effects (Preprint)

2018 ◽  
Author(s):  
Taotao Wang ◽  
Mengyuan Ren ◽  
Ying Shen ◽  
Xiaorou Zhu ◽  
Xing Zhang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Support ◽  
College Students ◽  
Self Efficacy ◽  
Structural Equation ◽  
Mobile Apps ◽  
Mediating Effect ◽  
Activity Levels ◽  
Support Level ◽  
Physical Activity Levels

BACKGROUND Physical inactivity is a risk factor for chronic noncommunicable diseases. Insufficient physical activity has become an important public health problem worldwide. As mobile apps have rapidly developed, physical activity apps have the potential to improve the level of physical activity among populations. OBJECTIVE This study aimed to evaluate the effect of physical activity apps on levels of physical activity among college students. METHODS A Web-based questionnaire was used to survey college students in Beijing from December 27, 2017, to January 5, 2018. According to a previous survey, 43% of college students using physical activity apps and 36% of those who never used such apps achieved the physical activity recommendations. In this study, the sample size was calculated to be 500. The questionnaire consisted of 5 parts: the use of physical activity apps, sports habits, social support, self-efficacy, and social demographic information. Structural equation modeling was used to test the relationships between the use of physical activity apps, self-efficacy, social support, and level of physical activity. RESULTS Of the 1245 participants, 384 college students (30.8%) used physical activity apps (in the past month). Of these 384 students, 191 (49.7%) gained new friends via the app. College students who were using physical activity apps had a higher level of physical activity and higher scores for social support and self-efficacy (<italic>P</italic>&lt;.001) than those who did not use such apps. The use of physical activity apps significantly affected the mediating effect of physical activity level through social support (beta=.126; <italic>P</italic>&lt;.001) and self-efficacy (beta=.294; <italic>P</italic>&lt;.001). Gender played an important role in app use, self-efficacy, and physical activity in the mediation model: male users spent more time on physical activity and had higher self-efficacy scores (<italic>P</italic>&lt;.001). CONCLUSIONS This study focused on college students in Beijing and found that the use of physical activity apps is associated with higher physical activity levels among these students. This effect is mainly through the mediation effect of social support and self-efficacy, rather than the direct effect of physical activity apps. The use of physical activity apps is associated with a higher social support level and higher self-efficacy score. Furthermore, a high social support level and high self-efficacy score are associated with higher physical activity levels.

scholarly journals Towards a New Methodology to Capture the Legal Compatibility of Conversational Speech Agents

2020 ◽  
Author(s):  
Ernestine Dickhaut ◽  
Laura Friederike Thies ◽  
Andreas Janson ◽  
Alexander Roßnagel ◽  
Jan Marco Leimeister
Keyword(s):  
Conversational Speech

scholarly journals Depression-level assessment from multi-lingual conversational speech data using acoustic and text features

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Cenk Demiroglu ◽  
Aslı Beşirli ◽  
Yasin Ozkanca ◽  
Selime Çelik
Keyword(s):  
Text Analysis ◽  
Training Data ◽  
Conversational Speech ◽  
Acoustic Feature ◽  
The World ◽  
Significant Burden ◽  
Depression Assessment ◽  
Depression Level ◽  
Text Features ◽  
Multiple Languages

AbstractDepression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.

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