In this paper, the researchers study how to improve the accuracy of device-directed speech detection (DDSD) systems, which distinguish between voice assistant queries and side conversations or background speech. They explore fusion schemes to make the systems more robust when some of the verbal cues are unavailable in real-world settings.
Device-Directed Speech Detection: Enhancing AI Solutions for Middle Managers
Device-directed speech detection (DDSD) is the task of distinguishing between queries directed at a voice assistant and side conversations or background speech. State-of-the-art DDSD systems use verbal cues, such as acoustic, text, and automatic speech recognition system (ASR) features, to classify speech as device-directed or otherwise. However, these systems often face challenges when deployed in real-world settings where some modalities may be unavailable.
In our latest research, we investigate fusion schemes for DDSD systems that can be made more robust to missing modalities. By leveraging both verbal and non-verbal features, our Modality Dropout for Multimodal Device Directed Speech Detection approach offers practical solutions to enhance the accuracy and reliability of DDSD systems.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features
- Apple Machine Learning Research
- Twitter – @itinaicom
