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Weak Speech Supervision: A case study of Dysarthria Severity Classification

机译：弱演讲监督：痛经严重分类的案例研究

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Machine Learning methodologies are making a remarkable contribution, and yielding state-of-the-art results in different speech domains. With this exceptionally significant achievement, a large amount of labeled data is the largest bottleneck in the deployment of these speech systems. To generate massive data, hand-labeling training data is an intensively laborious task. This is problematic for clinical applications where obtaining such data labeled by speech pathologists is expensive and time-consuming. To overcome these problems, we introduce a new paradigm called Weak Speech Supervision (WSS), a first-of-its-kind system that helps users to train state-of-the-art classification models without hand-labeling training data. Users can write labeling functions (i.e., weak rules) to generate weak data from the unlabeled training set. In this paper, we provide the efficiency of this methodology via showing the case study of the severity-based binary classification of dysarthric speech. In WSS, we train a classifier on trusted data (labeled with 100% accuracy) via utilizing the weak data (labeled using weak supervision) to make our classifier model more efficient. Analysis of the proposed methodology is performed on Universal Access (UA) corpus. We got on an average 35.68% and 43.83% relative improvement in terms of accuracy and F1-score w.r.t. baselines, respectively.
机译：机器学习方法在不同的语音域中做出了显着的贡献，并产生了最先进的结果。通过这种特别重要的成就，大量标记数据是部署这些语音系统的最大瓶颈。为了产生大规模数据，手工标签培训数据是一个集中艰难的任务。对于临床应用，这是有问题的，其中获得由语音病理学家标记的这些数据昂贵且耗时的数据。为了克服这些问题，我们介绍了一个名为弱演讲监督（WSS）的新的范式，这是一流的系统，可以帮助用户培训最先进的分类模型，而无需手动标记训练数据。用户可以编写标签函数（即，弱规则）来从未标记的培训集生成弱数据。在本文中，我们通过表明对发育性语音的严重性的二进制分类来提供该方法的效率。在WSS中，我们通过利用弱数据（使用弱监管标记）培训有信任数据（标记为100％的准确度）的分类器，使我们的分类器模型更高效。对普遍访问（UA）语料库进行提出的方法的分析。我们平均35.68％和43.83％的相对改善，准确性和F1-得分W.R.T.T.基准分别。

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