AP18-OLR Challenge: Three Tasks and Their Baselines
원문 초록
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles. Following the events in the last two years, namely AP16-OLR and AP17-OLR, the challenge this year focuses on more challenging tasks, including (1) short-duration utterances, (2) confusing languages, and (3) open-set recognition. The same as the previous events, the data of AP18-OLR is also provided by SpeechOcean and the NSFC M2ASR project. Baselines based on both the i-vector model and neural networks are constructed for the participants' reference. We report the baseline results on the three tasks and demonstrate that the three tasks are truly challenging. All the data is free for participants, and the Kaldi recipes for the baselines have been published online.
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ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets
Synthetic Non-stationary Data Streams for Recognition of the Unknown
BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization
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