Automatic detection of vowel nasalization using knowledge‐based acoustic parameters

2006 ◽  
Vol 120 (5) ◽  
pp. 3377-3377
Author(s):  
Tarun Pruthi ◽  
Carol Y. Espy‐Wilson
Keyword(s):  
Automatic Detection ◽  
Acoustic Parameters ◽  
Knowledge Based ◽  
Vowel Nasalization

A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images

2015 ◽  
Vol 10 (11) ◽  
pp. 1737-1752 ◽  
Author(s):  
Abhishek Gupta ◽  
Om Prakash Kharbanda ◽  
Viren Sardana ◽  
Rajiv Balachandran ◽  
Harish Kumar Sardana
Keyword(s):  
Automatic Detection ◽  
Knowledge Based

Automatic detection and comparison of vowel nasalization in American English.

2010 ◽  
Vol 128 (4) ◽  
pp. 2291-2291 ◽  
Author(s):  
Jiahong Yuan ◽  
Amanda Seidl ◽  
Alejandrina Cristiá
Keyword(s):  
American English ◽  
Automatic Detection ◽  
Vowel Nasalization

scholarly journals Modelling automatic detection of prosodic boundaries for brazilian portuguese spontaneous speech

2020 ◽  
Vol 9 ◽  
pp. 105-128
Author(s):  
Tommaso Raso ◽  
Bárbara Teixeira ◽  
Plínio Barbosa
Keyword(s):  
Information Structure ◽  
Speech Rate ◽  
Automatic Detection ◽  
Spontaneous Speech ◽  
Brazilian Portuguese ◽  
Acoustic Parameters ◽  
Linear Discriminant ◽  
Two Samples ◽  
Prosodic Boundaries ◽  
Prosodic Boundary

Speech is segmented into intonational units marked by prosodic boundaries. This segmentation is claimed to have important consequences on syntax, information structure and cognition. This work aims both to investigate the phonetic-acoustic parameters that guide the production and perception of prosodic boundaries, and to develop models for automatic detection of prosodic boundaries in male monological spontaneous speech of Brazilian Portuguese. Two samples were segmented into intonational units by two groups of trained annotators. The boundaries perceived by the annotators were tagged as either terminal or non-terminal. A script was used to extract 111 phonetic-acoustic parameters along speech signal in a right and left windows around the boundary of each phonological word. The extracted parameters comprise measures of (1) Speech rate and rhythm; (2) Standardized segment duration; (3) Fundamental frequency; (4) Intensity; (5) Silent pause. The script considers as prosodic boundary positions at which at least 50% of the annotators indicated a boundary of the same type. A training of models composed by the parameters extracted by the script was developed; these models, were then improved heuristically. The models were developed from the two samples and from the whole data, both using non-balanced and balanced data. Linear Discriminant Analysis algorithm was adopted to produce the models. The models for terminal boundaries show a much higher performance than those for non-terminal ones. In this paper we: (i) show the methodological procedures; (ii) analyze the different models; (iii) discuss some strategies that could lead to an improvement of our results.

Knowledge-based automatic detection of multitype lung nodules from multidetector CT studies

2002 ◽  
Author(s):  
JianZhong Qian ◽  
Li Fan ◽  
Guo-Qing Wei ◽  
Carol L. Novak ◽  
Benjamin L. Odry ◽  
...  
Keyword(s):  
Multidetector Ct ◽  
Automatic Detection ◽  
Lung Nodules ◽  
Knowledge Based ◽  
Ct Studies

Acoustic parameters for automatic detection of nasal manner

2004 ◽  
Vol 43 (3) ◽  
pp. 225-239 ◽  
Author(s):  
Tarun Pruthi ◽  
Carol Y. Espy-Wilson
Keyword(s):  
Automatic Detection ◽  
Acoustic Parameters

Automatic detection of equipment alarms in a neonatal intensive care unit environment: a knowledge-based approach

2015 ◽  
Author(s):  
Ganna Raboshchuk ◽  
Peter Jančovič ◽  
Climent Nadeu ◽  
Alex Peiró Lilja ◽  
Münevver Köküer ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Automatic Detection ◽  
Knowledge Based

scholarly journals A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech

2018 ◽  
Vol 15 (2) ◽  
pp. 130-138 ◽  
Author(s):  
Laszlo Toth ◽  
Ildiko Hoffmann ◽  
Gabor Gosztolya ◽  
Veronika Vincze ◽  
Greta Szatloczki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Speech Recognition ◽  
Screening Method ◽  
Automatic Detection ◽  
Spontaneous Speech ◽  
Machine Learning Algorithms ◽  
Control Group ◽  
Acoustic Parameters

Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.

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