GraphemeAug: A Systematic Approach to Synthesized Hard Negative Keyword Spotting Examples

• URL: http://arxiv.org/abs/2505.14814v2
• Date: Sun, 25 May 2025 03:06:52 GMT
• Title: GraphemeAug: A Systematic Approach to Synthesized Hard Negative Keyword Spotting Examples
• Authors: Harry Zhang, Kurt Partridge, Pai Zhu, Neng Chen, Hyun Jin Park, Dhruuv Agarwal, Quan Wang,
• Abstract summary: Spoken Keyword Spotting (KWS) is the task of distinguishing between the presence and absence of a keyword in audio.<n>We propose a method to generate adversarial examples close to the decision boundary by making insertion/deletion/substitution edits on the keyword's graphemes.<n>We show that the technique improves AUC on a dataset of synthetic hard negatives by 61% while maintaining quality on positives and ambient negative audio data.
• Score: 9.34501666048989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spoken Keyword Spotting (KWS) is the task of distinguishing between the presence and absence of a keyword in audio. The accuracy of a KWS model hinges on its ability to correctly classify examples close to the keyword and non-keyword boundary. These boundary examples are often scarce in training data, limiting model performance. In this paper, we propose a method to systematically generate adversarial examples close to the decision boundary by making insertion/deletion/substitution edits on the keyword's graphemes. We evaluate this technique on held-out data for a popular keyword and show that the technique improves AUC on a dataset of synthetic hard negatives by 61% while maintaining quality on positives and ambient negative audio data.

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