H
Hari Krishna Vydana
Researcher at International Institute of Information Technology, Hyderabad
Publications - 35
Citations - 289
Hari Krishna Vydana is an academic researcher from International Institute of Information Technology, Hyderabad. The author has contributed to research in topics: Language identification & Hidden Markov model. The author has an hindex of 9, co-authored 34 publications receiving 218 citations. Previous affiliations of Hari Krishna Vydana include Velagapudi Ramakrishna Siddhartha Engineering College & Brno University of Technology.
Papers
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Journal ArticleDOI
Curriculum learning based approach for noise robust language identification using DNN with attention
TL;DR: In comparison to multi-SNR models, the LID systems trained with curriculum learning have performed better in terms of equal error rate (EER) and generalization in EER across varying background environments.
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Vowel-Based Non-uniform Prosody Modification for Emotion Conversion
TL;DR: In this work, performance of emotion conversion using the linear modification model is improved by using vowel-based non-uniform prosody modification to develop a rule-based emotion conversion method for a better emotional perception.
Proceedings ArticleDOI
Significance of neural phonotactic models for large-scale spoken language identification
TL;DR: Experiments show that the convex combination of statistical and recurrent neural network language model (RNNLM) based phonotactic models significantly outperform a strong baseline system of Deep Neural Network (DNN) which is shown to surpass the performance of i-vector based approach for LID.
Journal ArticleDOI
Significance of GMM-UBM based Modelling for Indian Language Identification☆
TL;DR: In this work, phonotactic variations imparted by the different languages are modelled using Gaussian mixture modelling with a universal background model (GMM-UBM) technique and performance of the proposed GMM-UBm based LID system is compared with conventional GMM based L ID system.
Proceedings ArticleDOI
Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery.
TL;DR: In this paper, a Bayesian Subspace Hidden Markov Model (SHMM) is used to learn the notion of acoustic units from the labeled data and then the model uses its knowledge to find new acoustic units on the target language.