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Found 3 papers, 0 papers with code
Bridging the Gap between Audio and Text using Parallel-attention for User-defined Keyword Spotting
This paper proposes a novel user-defined keyword spotting framework that accurately detects audio keywords based on text enrollment.
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy
Our approach overcomes this limitation by considering both the sentence-level semantics and the document-level rhetorical structure, resulting in a more robust and style-agnostic approach to detecting political bias in news articles.
Metric Learning for User-defined Keyword Spotting
In particular, we make the following contributions: (1) we construct a large-scale keyword dataset with an existing speech corpus and propose a filtering method to remove data that degrade model training; (2) we propose a metric learning-based two-stage training strategy, and demonstrate that the proposed method improves the performance on the user-defined keyword spotting task by enriching their representations; (3) to facilitate the fair comparison in the user-defined KWS field, we propose unified evaluation protocol and metrics.
