The Talking Robot: Distortion-Robust Acoustic Models for Robot-Robot Communication
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911495451836416 |
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| author | Li, Hanlong Kamalahasan, Karishma Li, Jiahui Nakadai, Kazuhiro Kousik, Shreyas |
| author_facet | Li, Hanlong Kamalahasan, Karishma Li, Jiahui Nakadai, Kazuhiro Kousik, Shreyas |
| contents | We present Artoo, a learned acoustic communication system for robots that replaces hand-designed signal processing with end-to-end co-trained neural networks. Our system pairs a lightweight text-to-speech (TTS) transmitter (1.18M parameters) with a conformer-based automatic speech recognition (ASR) receiver (938K parameters), jointly optimized through a differentiable channel. Unlike human speech, robot-to-robot communication is paralinguistics-free: the system need not preserve timbre, prosody, or naturalness, only maximize decoding accuracy under channel distortion. Through a three-phase co-training curriculum, the TTS transmitter learns to produce distortion-robust acoustic encodings that surpass the baseline under noise, achieving 8.3% CER at 0 dB SNR. The entire system requires only 2.1M parameters (8.4 MB) and runs in under 13 ms end-to-end on a CPU, making it suitable for deployment on resource-constrained robotic platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07072 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Talking Robot: Distortion-Robust Acoustic Models for Robot-Robot Communication Li, Hanlong Kamalahasan, Karishma Li, Jiahui Nakadai, Kazuhiro Kousik, Shreyas Robotics Machine Learning We present Artoo, a learned acoustic communication system for robots that replaces hand-designed signal processing with end-to-end co-trained neural networks. Our system pairs a lightweight text-to-speech (TTS) transmitter (1.18M parameters) with a conformer-based automatic speech recognition (ASR) receiver (938K parameters), jointly optimized through a differentiable channel. Unlike human speech, robot-to-robot communication is paralinguistics-free: the system need not preserve timbre, prosody, or naturalness, only maximize decoding accuracy under channel distortion. Through a three-phase co-training curriculum, the TTS transmitter learns to produce distortion-robust acoustic encodings that surpass the baseline under noise, achieving 8.3% CER at 0 dB SNR. The entire system requires only 2.1M parameters (8.4 MB) and runs in under 13 ms end-to-end on a CPU, making it suitable for deployment on resource-constrained robotic platforms. |
| title | The Talking Robot: Distortion-Robust Acoustic Models for Robot-Robot Communication |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2603.07072 |