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20172024
most citedTime-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

32 citations · 58 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.SD201932 cited

Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

Achintya kr. Sarkar, Zheng-Hua Tan, Hao Tang +2

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for im…

cs.SD2018

Domain Mismatch Robust Acoustic Scene Classification using Channel Information Conversion

Seongkyu Mun, Suwon Shon

In a recent acoustic scene classification (ASC) research field, training and test device channel mismatch have become an issue for the real world implementation. To address the iss…

cs.SD2018

Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition

Suwon Shon, Ahmed Ali, James Glass

Dialect identification (DID) is a special case of general language identification (LID), but a more challenging problem due to the linguistic similarity between dialects. In this p…

cs.SD20172 cited

Recursive Whitening Transformation for Speaker Recognition on Language Mismatched Condition

Suwon Shon, Seongkyu Mun, Hanseok Ko

Recently in speaker recognition, performance degradation due to the channel domain mismatched condition has been actively addressed. However, the mismatches arising from language i…

cs.SD20173 cited

Autoencoder based Domain Adaptation for Speaker Recognition under Insufficient Channel Information

Suwon Shon, Seongkyu Mun, Wooil Kim +1

In real-life conditions, mismatch between development and test domain degrades speaker recognition performance. To solve the issue, many researchers explored domain adaptation appr…

cs.SD201714 cited

DNN Transfer Learning based Non-linear Feature Extraction for Acoustic Event Classification

Seongkyu Mun, Minkyu Shin, Suwon Shon +3

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases…