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This study introduces a new method for service robots to understand spoken commands robustly, even with speech recognition errors. The novel approach enhances semantic parsing performance for robot-directed speech commands.

Keywords:
language understandingrobot-directed speech detectionsemantic parsingservice robotspeech recognition

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Service robots require robust understanding of spoken commands for effective human interaction.
  • Conventional methods struggle with speech recognition errors, hindering semantic parsing accuracy.
  • Existing approaches often fail when automatic speech recognition (ASR) systems produce errors.

Purpose of the Study:

  • To develop a robust method for service robots to understand spoken commands despite ASR errors.
  • To improve semantic parsing performance in robot-directed speech command understanding.
  • To mitigate the impact of speech recognition inaccuracies on robot command interpretation.

Main Methods:

  • Utilized encoder-decoder neural networks, specifically sequence-to-sequence models, with injected noise.
  • Noise was introduced into phoneme sequences during the training of the semantic parsing system.
  • Evaluated the proposed method, Sequence to Sequence with Noise Injection (Seq2Seq-NI), on General Purpose Service Robot (GPSR) tasks.

Main Results:

  • The Seq2Seq-NI method significantly outperformed baseline methods in understanding spoken commands.
  • The approach demonstrated improved semantic parsing accuracy in the presence of ASR errors.
  • Seq2Seq-NI enabled robots to correctly interpret commands even with faulty speech recognition outputs.

Conclusions:

  • The proposed Seq2Seq-NI method offers a robust solution for service robot spoken command understanding.
  • Injecting noise during training effectively enhances the resilience of semantic parsing to ASR errors.
  • This advancement is crucial for improving the reliability and performance of service robots in real-world applications.