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20172023
most citedOn Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

303 citations · 633 across the 29 of their papers we have counts for

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Showing 2020 · eess.ASShow all

7 papers · 2 filters

eess.AS2020

The Third DIHARD Diarization Challenge

Neville Ryant, Prachi Singh, Venkat Krishnamohan +6

DIHARD III was the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variability in recording equipment, noise condit…

eess.AS2020

Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis

Desh Raj, Pavel Denisov, Zhuo Chen +11

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in syst…

eess.AS202046 cited

Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation

Hu Hu, Chao-Han Huck Yang, Xianjun Xia +13

In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Chal…

eess.AS2020303 cited

On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

Jun Qi, Jun Du, Sabato Marco Siniscalchi +2

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work…

eess.AS2020

An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances

Hu Hu, Sabato Marco Siniscalchi, Yannan Wang +3

In this paper, we propose a sub-utterance unit selection framework to remove acoustic segments in audio recordings that carry little information for acoustic scene classification (…

eess.AS2020

Third DIHARD Challenge Evaluation Plan

Neville Ryant, Kenneth Church, Christopher Cieri +3

This paper introduces the third DIHARD challenge, the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in…