Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Rashmi Kethireddy"'
Autor:
Vijaya Lakshmi V. Nadimpalli, Santosh Kesiraju, Rohith Banka, Rashmi Kethireddy, Suryakanth V. Gangashetty
Publikováno v:
IEEE Access, Vol 10, Pp 34789-34799 (2022)
This paper presents the resources and benchmarks developed for keyword search (KWS) in spoken audio from six low-resource Indian languages (from two families), namely Gujarati, Hindi, Marathi, Odia, Tamil, and Telugu. The current work on constructing
Externí odkaz:
https://doaj.org/article/d93283f13a28477daf0ba76fd75e35b8
Publikováno v:
IEEE Access, Vol 8, Pp 174871-174879 (2020)
In this study, we propose Mel-weighted single frequency filtering (SFF) spectrograms for dialect identification. The spectrum derived using SFF has high spectral resolution for harmonics and resonances while simultaneously maintaining good time-resol
Externí odkaz:
https://doaj.org/article/2dc2e1826381423daecf4e58784fb8bf
Publikováno v:
The Journal of the Acoustical Society of America. 151(2)
The goal of this study is to investigate advanced signal processing approaches [single frequency filtering (SFF) and zero-time windowing (ZTW)] with modern deep neural networks (DNNs) [convolution neural networks (CNNs), temporal convolution neural n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22afd44640f3185f25aa0ee0e7a08a13
https://pubmed.ncbi.nlm.nih.gov/35232068
https://pubmed.ncbi.nlm.nih.gov/35232068
Publikováno v:
INTERSPEECH
Parkinson's disease (PD) is a progressive deterioration of the human central nervous system. Detection of PD (discriminating patients with PD from healthy subjects) from speech is a useful approach due to its non-invasive nature. This study proposes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aed3435b193be59a63bb7838215852ef
https://doi.org/10.21437/interspeech.2020-3197
https://doi.org/10.21437/interspeech.2020-3197
Publikováno v:
IJCNN
Most of the applications in speech use mel-frequency spectral coefficients (MFSC) as features as they match the human perceptual mechanism, where the emphasis is given to vocal tract characteristics. But in accent classification, mel-scale distributi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe48659e81c7fd9249f73a25b5b1b663
https://aaltodoc.aalto.fi/handle/123456789/47472
https://aaltodoc.aalto.fi/handle/123456789/47472
Publikováno v:
Odyssey
In this paper, we propose to use novel acoustic features, namely zero-time windowing cepstral coefficients (ZTWCC) for dialect classification. ZTWCC features are derived from high resolution spectrum obtained with zero-time windowing (ZTW) method, an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d05a05045924f4dab3073a603b4650d6
https://aaltodoc.aalto.fi/handle/123456789/111182
https://aaltodoc.aalto.fi/handle/123456789/111182
Publikováno v:
Applied Acoustics. 188:108553
Funding Information: The first author would like to thank the University Grants Commission India (Project No. 3582/(NET-NOV2017)) for supporting her PhD. The second author would like to thank the Academy of Finland (Projects 313390 and 330139) for su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bed9260007c4882ac43ccbf2eb90860c
https://doi.org/10.1016/j.apacoust.2021.108553
https://doi.org/10.1016/j.apacoust.2021.108553