22HS4800, Development of semi-supervised learning language intelligence technology and Korean tutoring service for foreigners,
Lee Yunkeun
Abstract
Although advances in deep learning have brought remarkable improvements to Overlapped Speech Detection (OSD), the performance in far-field environments is still limited owing to the lack of real-world overlapped speech and a low signal-to-noise ratio. In this paper, we present an end-to-end audiovisual OSD system based on decision fusion between audio and video modalities. Firstly, we propose a simple yet powerful audio data augmentation method for spontaneous distant speech data. Secondly, to maximize the effectiveness of the video modality, we design a video OSD system based on a cross-speaker attention module that explores the visual correlation between multiple speakers. Lastly, we present cross-modality attention module to make the final decision more accurate. Our experimental results demonstrate that our approach outperforms current state-of-the-art methods on a real-world distant speech dataset. Moreover, our approach can robustly detect overlapped speech when compared with its counterpart, which uses audio modality alone.
KSP Keywords
Audio and video, Audio data, Audio-visual, Augmentation method, Current state, Data Augmentation, Decision Fusion, End to End(E2E), Far-field, Field Environment, Real-world
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
Copyright Policy
ETRI KSP Copyright Policy
The materials provided on this website are subject to copyrights owned by ETRI and protected by the Copyright Act. Any reproduction, modification, or distribution, in whole or in part, requires the prior explicit approval of ETRI. However, under Article 24.2 of the Copyright Act, the materials may be freely used provided the user complies with the following terms:
The materials to be used must have attached a Korea Open Government License (KOGL) Type 4 symbol, which is similar to CC-BY-NC-ND (Creative Commons Attribution Non-Commercial No Derivatives License). Users are free to use the materials only for non-commercial purposes, provided that original works are properly cited and that no alterations, modifications, or changes to such works is made. This website may contain materials for which ETRI does not hold full copyright or for which ETRI shares copyright in conjunction with other third parties. Without explicit permission, any use of such materials without KOGL indication is strictly prohibited and will constitute an infringement of the copyright of ETRI or of the relevant copyright holders.
J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
If you have any questions or concerns about these terms of use, or if you would like to request permission to use any material on this website, please feel free to contact us
KOGL Type 4:(Source Indication + Commercial Use Prohibition+Change Prohibition)
Contact ETRI, Research Information Service Section
Privacy Policy
ETRI KSP Privacy Policy
ETRI does not collect personal information from external users who access our Knowledge Sharing Platform (KSP). Unathorized automated collection of researcher information from our platform without ETRI's consent is strictly prohibited.
[Researcher Information Disclosure] ETRI publicly shares specific researcher information related to research outcomes, including the researcher's name, department, work email, and work phone number.
※ ETRI does not share employee photographs with external users without the explicit consent of the researcher. If a researcher provides consent, their photograph may be displayed on the KSP.