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Items: 1 to 20 of 81

1.

Selection of spectral compressive operator for vector Taylor series-based model adaptation in noisy environments.

Baek S, Kang HG.

J Acoust Soc Am. 2014 Jun;135(6):EL284-90. doi: 10.1121/1.4874358.

PMID:
24907835
2.

Nonlinear spectro-temporal features based on a cochlear model for automatic speech recognition in a noisy situation.

Choi YS, Lee SY.

Neural Netw. 2013 Sep;45:62-9. doi: 10.1016/j.neunet.2013.02.006. Epub 2013 Mar 7.

PMID:
23558292
3.

Noisy speech recognition using de-noised multiresolution analysis acoustic features.

Chan CP, Ching PC, Lee T.

J Acoust Soc Am. 2001 Nov;110(5 Pt 1):2567-74.

PMID:
11757946
4.

Auditory-model based robust feature selection for speech recognition.

Koniaris C, Kuropatwinski M, Kleijn WB.

J Acoust Soc Am. 2010 Feb;127(2):EL73-9. doi: 10.1121/1.3284545.

PMID:
20136182
5.

Robustness of auditory Teager Energy Cepstrum Coefficients for classification of pathological and normal voices in noisy environments.

Salhi L, Cherif A.

ScientificWorldJournal. 2013 May 28;2013:435729. doi: 10.1155/2013/435729. Print 2013.

6.

A bio-inspired feature extraction for robust speech recognition.

Zouhir Y, Ouni K.

Springerplus. 2014 Nov 4;3:651. doi: 10.1186/2193-1801-3-651. eCollection 2014.

7.

Performance evaluation and enhancement of lung sound recognition system in two real noisy environments.

Chang GC, Lai YF.

Comput Methods Programs Biomed. 2010 Feb;97(2):141-50. doi: 10.1016/j.cmpb.2009.06.002. Epub 2009 Jul 16.

PMID:
19615782
8.

Auditory models of suprathreshold distortion and speech intelligibility in persons with impaired hearing.

Bernstein JG, Summers V, Grassi E, Grant KW.

J Am Acad Audiol. 2013 Apr;24(4):307-28. doi: 10.3766/jaaa.24.4.6.

PMID:
23636211
9.

Modulation frequency features for phoneme recognition in noisy speech.

Ganapathy S, Thomas S, Hermansky H.

J Acoust Soc Am. 2009 Jan;125(1):EL8-12. doi: 10.1121/1.3040022.

PMID:
19173383
10.

Analysis of acoustic parameters for consonant voicing classification in clean and telephone speech.

Lee SM, Choi JY.

J Acoust Soc Am. 2012 Mar;131(3):EL197-202. doi: 10.1121/1.3678667.

PMID:
22423808
11.

Evaluation of formant-like features on an automatic vowel classification task.

de Wet F, Weber K, Boves L, Cranen B, Bengio S, Bourlard H.

J Acoust Soc Am. 2004 Sep;116(3):1781-92.

PMID:
15478445
12.

Feature extraction based on mel-scaled wavelet transform for heart sound analysis.

Wang P, Kim Y, Soh C.

Conf Proc IEEE Eng Med Biol Soc. 2005;7:7572-5.

PMID:
17282033
13.

Cepstral representation of speech motivated by time-frequency masking: an application to speech recognition.

Aikawa K, Singer H, Kawahara H, Tohkura Y.

J Acoust Soc Am. 1996 Jul;100(1):603-14.

PMID:
8675851
14.

Spectro-temporal modulation subspace-spanning filter bank features for robust automatic speech recognition.

Schädler M, Meyer BT, Kollmeier B.

J Acoust Soc Am. 2012 May;131(5):4134-51. doi: 10.1121/1.3699200.

PMID:
22559385
15.

Analysis and prediction of acoustic speech features from mel-frequency cepstral coefficients in distributed speech recognition architectures.

Darch J, Milner B, Vaseghi S.

J Acoust Soc Am. 2008 Dec;124(6):3989-4000. doi: 10.1121/1.2997436.

PMID:
19206822
16.

Exploring the relationship between spectral and cepstral measures of voice and the Voice Handicap Index (VHI).

Awan SN, Roy N, Cohen SM.

J Voice. 2014 Jul;28(4):430-9. doi: 10.1016/j.jvoice.2013.12.008. Epub 2014 Mar 31.

PMID:
24698884
17.

Real-time spectrum estimation-based dual-channel speech-enhancement algorithm for cochlear implant.

Chen Y, Gong Q.

Biomed Eng Online. 2012 Sep 24;11:74. doi: 10.1186/1475-925X-11-74.

18.

New features using robust MVDR spectrum of filtered autocorrelation sequence for robust speech recognition.

Seyedin S, Ahadi SM, Gazor S.

ScientificWorldJournal. 2013 Dec 31;2013:634160. doi: 10.1155/2013/634160. eCollection 2013.

19.

Narrow-band autocorrelation function features for the automatic recognition of acoustic environments.

Valero X, Alías F.

J Acoust Soc Am. 2013 Jul;134(1):880-90. doi: 10.1121/1.4807807.

PMID:
23862894

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