r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled Noise Introducing and Contextual Information Incorporation

Zhao, Chendong; Wang, Jianzong; Qu, Xiaobo; Wang, Haoqian; Xiao, Jing

doi:10.1109/icassp43922.2022.9746051

Cited by 9 publications

(1 citation statement)

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“…Environmental sound is naturally different from other audios. It doesn't exhibit any stationary patterns like phoneme in speech [3], [4] or rhythm in music. In contrast, sound event contains very complex temporal structure [5]- [7] that may be continues (e.g.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection

Zhao¹,

Wang²,

Li³

et al. 2022

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Sound event detection is to infer the event by understanding the surrounding environmental sounds. Due to the scarcity of rare sound events, it becomes challenging for the well-trained detectors which have learned too much prior knowledge. Meanwhile, few-shot learning methods promise a good generalization ability when facing a new limited-data task. Recent approaches have achieved promising results in this field. However, these approaches treat each support example independently, ignoring the information of other examples from the whole task. Because of this, most of previous methods are constrained to generate a same feature embedding for all test-time tasks, which is not adaptive to each inputted data. In this work, we propose a novel task-adaptive module which is easy to plant into any metric-based few-shot learning frameworks. The module could identify the task-relevant feature dimension. Incorporating our module improves the performance considerably on two datasets over baseline methods, especially for the transductive propagation network. Such as +6.8% for 5-way 1-shot accuracy on ESC-50, and +5.9% on noiseESC-50. We investigate our approach in the domain-mismatch setting and also achieve better results than previous methods.

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