Deep neural architectures for dialect classification with single frequency filtering and zero-time windowing feature representations-Reference-Cited by-同舟云学术

Deep neural architectures for dialect classification with single frequency filtering and zero-time windowing feature representations

Published:2022-02 Issue:2 Volume:151 Page:1077-1092
ISSN:0001-4966
Container-title:The Journal of the Acoustical Society of America
language:en
Short-container-title:The Journal of the Acoustical Society of America

Author:

Kethireddy Rashmi¹^ORCID,Kadiri Sudarsana Reddy²^ORCID,Gangashetty Suryakanth V.³^ORCID

Affiliation:

1. Speech Processing Laboratory, International Institute of Information Technology-Hyderabad (IIIT-H), 500032, India

2. Department of Signal Processing and Acoustics, Aalto University, Otakaari 3, FI-00076 Espoo, Finland

3. Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502, Andhra Pradesh, India

Funder

university grant commission-net

academy of finland

Publisher

Acoustical Society of America (ASA)

Subject

Acoustics and Ultrasonics,Arts and Humanities (miscellaneous)

Link

https://asa.scitation.org/doi/pdf/10.1121/10.0009405

Reference54 articles.

1. Single Frequency Filtering Approach for Discriminating Speech and Nonspeech

2. A study of temporal features and frequency characteristics in American English foreign accent

3. Bai, S. , Kolter, J. Z. , and Koltun, V. (2018). “ An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv:1803.01271.

4. i-Vector Modeling of Speech Attributes for Automatic Foreign Accent Recognition

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