Literature DB >> 17946059

Pathological voice assessment.

Alireza A Dibazar1, Theodore W Berger, Shrikanth S Narayanan.   

Abstract

While there are number of guidelines and methods used in practice, there is no standard universally agreed upon system for assessment of pathological voices. Pathological voices are primarily labeled based on the perceptual judgments of specialists, a process that may result in different label(s) being assigned to a given voice sample. This paper focuses on the recognition of five specific pathologies. The main goal is to compare two different classification methods. The first method considers single label classification by assigning a new label (single label) to the ensembles to which they most likely belong. The second method employs all labels originally assigned to the voice samples. Our results show that the pathological voice assessment performance in the second method is improved with respect to the first method.

Mesh:

Year:  2006        PMID: 17946059     DOI: 10.1109/IEMBS.2006.259835

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  5 in total

1.  Automatic intelligibility classification of sentence-level pathological speech.

Authors:  Jangwon Kim; Naveen Kumar; Andreas Tsiartas; Ming Li; Shrikanth S Narayanan
Journal:  Comput Speech Lang       Date:  2015-01       Impact factor: 1.899

2.  Formant analysis in dysphonic patients and automatic Arabic digit speech recognition.

Authors:  Ghulam Muhammad; Tamer A Mesallam; Khalid H Malki; Mohamed Farahat; Mansour Alsulaiman; Manal Bukhari
Journal:  Biomed Eng Online       Date:  2011-05-30       Impact factor: 2.819

3.  Robust Detection of COVID-19 in Cough Sounds: Using Recurrence Dynamics and Variable Markov Model.

Authors:  Pauline Mouawad; Tammuz Dubnov; Shlomo Dubnov
Journal:  SN Comput Sci       Date:  2021-01-12

4.  An Analytical Study of Speech Pathology Detection Based on MFCC and Deep Neural Networks.

Authors:  Mohammed Zakariah; Reshma B; Yousef Ajmi Alotaibi; Yanhui Guo; Kiet Tran-Trung; Mohammad Mamun Elahi
Journal:  Comput Math Methods Med       Date:  2022-04-04       Impact factor: 2.809

5.  A preliminary study on improving the recognition of esophageal speech using a hybrid system based on statistical voice conversion.

Authors:  Othman Lachhab; Joseph Di Martino; Elhassane Ibn Elhaj; Ahmed Hammouch
Journal:  Springerplus       Date:  2015-10-26
  5 in total

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