Literature DB >> 17405369

Acoustic analysis and detection of hypernasality using a group delay function.

P Vijayalakshmi1, M Ramasubba Reddy, Douglas O'Shaughnessy.   

Abstract

In this paper, we describe a group delay-based signal processing technique for the analysis and detection of hypernasal speech. Our preliminary acoustic analysis on nasalized vowels shows that, even though additional resonances are introduced at various frequency locations, the introduction of a new resonance in the low-frequency region (around 250 Hz) is found to be consistent. This observation is further confirmed by a perceptual analysis carried out on vowel sounds that are modified by introducing different nasal resonances, and an acoustic analysis on hypernasal speech. Based on this, for subsequent experiments the focus is given only to the low-frequency region. The additive property of the group delay function can be exploited to resolve two closely spaced formants. However, when the formants are very close with considerably wider bandwidths as in hypernasal speech, the group delay function also fails to resolve. To overcome this, we suggest a band-limited approach to estimate the locations of the formants. Using the band-limited group delay spectrum, we define a new acoustic measure for the detection of hypernasality. Experiments are carried out on the phonemes /a/, /i/, and /u/ uttered by 33 hypernasal speakers and 30 normal speakers. Using the group delay-based acoustic measure, the performance on a hypernasality detection task is found to be 100% for /a/, 88.78% for /i/ and 86.66% for /u/. The effectiveness of this acoustic measure is further cross-verified on a speech data collected in an entirely different recording environment.

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Year:  2007        PMID: 17405369     DOI: 10.1109/TBME.2006.889191

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  8 in total

1.  Automatic evaluation of hypernasality based on a cleft palate speech database.

Authors:  Ling He; Jing Zhang; Qi Liu; Heng Yin; Margaret Lech; Yunzhi Huang
Journal:  J Med Syst       Date:  2015-03-28       Impact factor: 4.460

2.  Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features.

Authors:  Michael Saxon; Ayush Tripathi; Yishan Jiao; Julie Liss; Visar Berisha
Journal:  IEEE/ACM Trans Audio Speech Lang Process       Date:  2020-08-07

3.  [Establishment and application of mandarin cleft palate speech database].

Authors:  Ping-Chuan Ma; Bo-Chun Mao; Chun-Li Guo; Chen-Hao Yu; Ruo-Ling Li; Ling He; Heng Yin
Journal:  Hua Xi Kou Qiang Yi Xue Za Zhi       Date:  2020-04-01

4.  A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children With Cleft Palate.

Authors:  Vikram C Mathad; Nancy Scherer; Kathy Chapman; Julie M Liss; Visar Berisha
Journal:  IEEE Trans Biomed Eng       Date:  2021-09-20       Impact factor: 4.756

5.  Automatic initial and final segmentation in cleft palate speech of Mandarin speakers.

Authors:  Ling He; Yin Liu; Heng Yin; Junpeng Zhang; Jing Zhang; Jiang Zhang
Journal:  PLoS One       Date:  2017-09-19       Impact factor: 3.240

6.  Evaluation of noise excitation as a method for detection of hypernasality.

Authors:  Kat Young; Triona Sweeney; Rebecca R Vos; Felicity Mehendale; Helena Daffern
Journal:  Appl Acoust       Date:  2022-03-15       Impact factor: 2.639

7.  Assessment of hypernasality for children with cleft palate based on cepstrum analysis.

Authors:  Ehsan Akafi; Mansour Vali; Negin Moradi; Kowsar Baghban
Journal:  J Med Signals Sens       Date:  2013-10

8.  Acoustic analysis and detection of pharyngeal fricative in cleft palate speech using correlation of signals in independent frequency bands and octave spectrum prominent peak.

Authors:  Fei He; Xiyue Wang; Heng Yin; Han Zhang; Gang Yang; Ling He
Journal:  Biomed Eng Online       Date:  2020-05-27       Impact factor: 2.819

  8 in total

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