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End-to-end neural network models (E2E) have shown significant performancebenefits on different INTERSPEECH ComParE tasks. Prior work has applied eithera single instance of an E2E model for a task or the same E2E architecture fordifferent tasks. However, applying a single model is unstable or using the samearchitecture under-utilizes task-specific information. On ComParE 2020 tasks,we investigate applying an ensemble of E2E models for robust performance anddeveloping task-specific modifications for each task. ComParE 2020 introducesthree sub-challenges: the breathing sub-challenge to predict the output of arespiratory belt worn by a patient while speaking, the elderly sub-challenge toestimate the elderly speaker's arousal and valence levels and the masksub-challenge to classify if the speaker is wearing a mask or not. On each ofthese tasks, an ensemble outperforms the single E2E model. On the breathingsub-challenge, we study the impact of multi-loss strategies on taskperformance. On the elderly sub-challenge, predicting the valence and arousallevels prompts us to investigate multi-task training and implement datasampling strategies to handle class imbalance. On the mask sub-challenge, usingan E2E system without feature engineering is competitive to feature-engineeredbaselines and provides substantial gains when combined with feature-engineeredbaselines.

Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge