# Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing

arXiv: https://arxiv.org/abs/2507.12002 · HTML: https://mlsanity.com/paper/2507.12002/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 73 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Does a Technique for Building Multimodal Representation Matter? -- Comparative Analysis](https://mlsanity.com/paper/2206.06367/) (2022)
- [Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors](https://mlsanity.com/paper/2007.07172/) (2020)

## Abstract (arXiv)

Social interactions play a crucial role in shaping human behavior, relationships, and societies. It encompasses various forms of communication, such as verbal conversation, non-verbal gestures, facial expressions, and body language. In this work, we develop a novel computational approach to detect face-to-face verbal conversations, a foundational aspect of human social interactions. We leverage multimodal data captured by a commodity smartwatch, specifically synchronizing microphone audio with 6-axis inertial signals (accelerometer and gyroscope). We design, train, and evaluate convolutional and attention-based neural networks using three different fusion methods to integrate the audio and motion modalities. To validate this framework, we conduct a lab study with 11 participants and a semi-naturalistic study with 24 participants. Our comprehensive evaluation demonstrates that fusing inertial data with audio significantly improves detection performance by capturing non-verbal conversational dynamics. Overall, our framework achieved 82.0±3.0% macro F1-score when detecting conversations in the lab and 77.2±1.8% in the semi-naturalistic setting. Lastly, we demonstrate real-time conversation detection by deploying our trained model to a user application running on a commercial smartwatch.