Modeling concurrent vowel identification for shorter durations-Reference-Cited by-同舟云学术

Modeling concurrent vowel identification for shorter durations

Published:2020-12 Issue: Volume:125 Page:1-6
ISSN:0167-6393
Container-title:Speech Communication
language:en
Short-container-title:Speech Communication

Author:

Settibhaktini Harshavardhan,Chintanpalli Ananthakrishna

Funder

OPERA

BITS Pilani

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modeling and Simulation,Software

Reference29 articles.

1. Double-vowel perception in listeners with cochlear hearing loss: differences in fundamental frequency, ear of presentation, and relative amplitude;Arehart;J. Speech. Lang. Hear. Res.,2005

2. Pitches of concurrent vowels;Assmann;J. Acoust. Soc. Am.,1998

3. Modeling the perception of concurrent vowels: vowels with different fundamental frequencies;Assmann;J. Acoust. Soc. Am.,1990

4. The contribution of waveform interactions to the perception of concurrent vowels;Assmann;J. Acoust. Soc. Am.,1994

5. An autocorrelation model with place dependence to account for the effect of harmonic number on fundamental frequency discrimination;Bernstein;J. Acoust. Soc. Am.,2005

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1. Predicting Level-Dependent Changes in Concurrent Vowel Scores Using the 2D-CNN Models;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023

2. Modeling Concurrent Vowel Scores Using the Time Delay Neural Network and Multitask Learning;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2022

3. Modeling the effects of age and hearing loss on concurrent vowel scores;The Journal of the Acoustical Society of America;2021-11

4. Concurrent Vowel Identification Using the Deep Neural Network;Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021);2021-09-30