scholarly journals Automatic detection of discourse structure for speech recognition and understanding

2002 ◽  
Author(s):  
D. Jurafsky ◽  
R. Bates ◽  
N. Coccaro ◽  
R. Martin ◽  
M. Meteer ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Detection ◽  
Discourse Structure

Enriching speech recognition with automatic detection of sentence boundaries and disfluencies

2006 ◽  
Vol 14 (5) ◽  
pp. 1526-1540 ◽  
Author(s):  
Yang Liu ◽  
E. Shriberg ◽  
A. Stolcke ◽  
D. Hillard ◽  
M. Ostendorf ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Detection

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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