无声语音识别的质量感知聚合共形预测

Kai Xu, Wei Zhang, Ming Zhang, You Wang, Guang‐hua Li
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引用次数: 0

摘要

肌电图(electromyography, EMG)作为一种末端神经信号,在无声语音识别中有着广泛的应用。SSR任务可以转换为模式识别问题,其中无声语音模式对应于特征空间中的聚类。传统的基于电感共形预测器(ICP)的SSR解决方案在子采样方面存在局限性。此外,数据质量评估是长期以来对模型识别能力的挑战,但在以往的工作中很少讨论。我们引入聚合共形预测(ACP)来代替ICP,增强了子样本的多样性来解决这一问题。为了在数据扩展中挖掘出高质量的扩展数据集,本研究提出了监督k均值评价(SKE)方法。ACP框架与SKE方法相结合,提供了一种高效、稳健的SSR解决方案,并在汉语词汇任务中得到了验证。我们的研究结果支持了设计SKE方法和使用ACP框架的重要意义。据我们所知,本研究首次在SSR任务中应用ACP方法并定量评价数据质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quality-aware aggregated conformal prediction for silent speech recognition
As a kind of terminal neural signal, electromyography (EMG) generated by articulatory muscles is widely used in silent speech recognition (SSR). The SSR task can be converted to a pattern recognition problem, where silent speech patterns correspond to clusters in the feature space. Conventional inductive conformal predictor (ICP) based solutions in the SSR face limitations in the subsampling. In addition, the data quality assessment is a long-standing challenge on the recognition ability of models, but it is seldom discussed in previous works. We introduce the aggregated conformal prediction (ACP) to replace ICP, which enhances the diversity of subsampling to solve the problem. With the purpose of digging out the high-quality extended dataset in data expansion, this study proposes the Supervised K-means Evaluation (SKE) method. Equipped with SKE method, ACP framework contributes to an efficient and robust SSR solution, and the advantages have been validated on the task for Chinese words. Our results support it is significant to design SKE method and utilize ACP framework. To our knowledge, this study is the first to apply ACP methodology and quantitatively evaluate the data quality in the SSR task.
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