mlsanityData to 7 Oct 2026

The Talking Robot: Distortion-Robust Acoustic Models for Robot-Robot Communication

arXiv 2603.07072 ↗First posted 7 Mar 2026cs.RO cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 7 Mar 2026.

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10 of 19 references.

References · 10

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  2. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
  3. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  4. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
  5. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
  6. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 710
  7. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
  8. 2017An Introduction to Deep Learning for the Physical Layercited by 227
  9. 2018OFDM-Autoencoder for End-to-End Learning of Communications Systemscited by 28
  10. 2019Deep Learning for Channel Coding via Neural Mutual Information Estimationcited by 14

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