B. Xiang

Nanjing University of Science and Technology

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Owen Kimball

RTX (United States)

Yuxin Sun

Shanghai Jiao Tong University

Yulin Wang

Jinan University

C.-L. Kao

National Taiwan University

Holger Schwenk

Johns Hopkins University

L. Chen

Centre National de la Recherche Scientifique

D. Xu

Nanjing Sport Institute

Long Nguyen

Vietnam National University Ho Chi Minh City

Rishabh Iyer

University of Dallas

Spyros Matsoukas

Amazon (Germany)

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Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

RTX (United States)

Nanjing University of Science and Technology

Centre National de la Recherche Scientifique

Signal Processing (United States)

Université Paris-Sud

Johns Hopkins University

Boston University

Shanghai Jiao Tong University

Carnegie Mellon University

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Exploration of perceptually-based features for speaker recognition: An empirical study

Chinese Journal of Electronics (1996)

B. Xiang Ke Chen Hong Chi

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NISB-Map: Scalable Mapping With Neural Implicit Spatial Block

IEEE Robotics and Automation Letters (2023)

B. Xiang Yuxin Sun Zhongqu Xie Xiaolong Yang Yulin Wang

Recently, neural implicit representations have been applied in the mapping process of simultaneous localization and mapping (SLAM), accompanied by less storage overhead and continuous representation. Nevertheless, related methods use a single neural network to represent the whole scene, resulting in forgetting the observed regions caused by the limited capacity of a single network in the large-scale scene. Several methods encode the scene into implicit voxels to avoid parameter forgetting while the memory is sacrificed. In this letter, we introduce a scalable mapping framework that utilizes extensible Neural Implicit Spatial Blocks (NISB) with fixed size to cover the entire scene by incrementally creating multiple Multi-Layer Perceptron (MLP) networks. In evaluations against alternative methods on 3 datasets of indoor environments, our method Avoids forgetting the observed areas during the mapping process with a small memory footprint and smoothly updates the global map at 2 Hz.

Memory footprint

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10.1109/lra.2023.3289152

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Speech recognition in multiple languages and domains: the 2003 BBN/LIMSI EARS system

IEEE International Conference on Acoustics Speech and Signal Processing (2004)

Richard Schwartz Thomas Colthurst Nicolae Duta H. Gish Rishabh Iyer

We report on the results of the first evaluations for the BBN/LIMSI system under the new DARPA EARS program. The evaluations were carried out for conversational telephone speech (CTS) and broadcast news (BN) for three languages: English, Mandarin, and Arabic. In addition to providing system descriptions and evaluation results, the paper highlights methods that worked well across the two domains and those few that worked well on one domain but not the other. For the BN evaluations, which had to be run under 10 times real-time, we demonstrated that a joint BBN/LIMSI system with a time constraint achieved better results than either system alone.

Mandarin Chinese

10.1109/icassp.2004.1326654

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Citations (39)