面向INTERSPEECH 2020计算副语言学挑战赛的阿尔托端到端DNN系统
Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge
摘要：
端到端神经网络模型（E2E）在不同的INTERSPEECH ComparE任务上表现出显著的性能优势。以前的工作要么应用E2E模型的单个实例的任务或相同的E2E架构的不同任务。然而，应用单个模型是不稳定的，或者使用相同的体系结构，而没有充分利用特定于任务的信息。在ComparE 2020任务中，我们研究了应用E2E模型的集合来实现强大的性能，并为每个任务开发特定于任务的修改。ComparE 2020引入了三个子挑战：呼吸子挑战，用于预测患者在说话时佩戴的呼吸带的输出;老年子挑战，用于估计老年说话者的唤醒和效价水平;以及面罩子挑战，用于分类说话者是否佩戴面罩。在这些任务中的每一个上，集成都优于单个E2E模型。在呼吸子挑战中，我们研究了多损失策略对任务绩效的影响。在老年子挑战中，预测效价和唤醒水平促使我们研究多任务训练并实施数据采样策略来处理类不平衡。在掩模子挑战中，使用没有功能工程的E2E系统与功能工程基线相比具有竞争力，并且在与功能工程基线相结合时提供了大量收益。
英文摘要：
End-to-end neural network models (E2E) have shown significant performance benefits on different INTERSPEECH ComParE tasks. Prior work has applied either a single instance of an E2E model for a task or the same E2E architecture for different tasks. However, applying a single model is unstable or using the same architecture under-utilizes task-specific information. On ComParE 2020 tasks, we investigate applying an ensemble of E2E models for robust performance and developing task-specific modifications for each task. ComParE 2020 introduces three sub-challenges: the breathing sub-challenge to predict the output of a respiratory belt worn by a patient while speaking, the elderly sub-challenge to estimate the elderly speaker's arousal and valence levels and the mask sub-challenge to classify if the speaker is wearing a mask or not. On each of these tasks, an ensemble outperforms the single E2E model. On the breathing sub-challenge, we study the impact of multi-loss strategies on task performance. On the elderly sub-challenge, predicting the valence and arousal levels prompts us to investigate multi-task training and implement data sampling strategies to handle class imbalance. On the mask sub-challenge, using an E2E system without feature engineering is competitive to feature-engineered baselines and provides substantial gains when combined with feature-engineered baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
