MICbots: collecting large realistic datasets for speech and audio research using mobile robots

Abstract : Speech and audio signal processing research is a tale of data collection efforts and evaluation campaigns. Large benchmark datasets for automatic speech recognition (ASR) have been instrumental in the advancement of speech recognition technologies. However, when it comes to robust ASR, source separation, and localization, especially using microphone arrays, the perfect dataset is out of reach, and many different data collection efforts have each made different compromises between the conflicting factors in terms of realism, ground truth, and costs. Our goal here is to escape some of the most difficult trade-offs by proposing MICbots, a low-cost method of collecting large amounts of realistic data where annotations and ground truth are readily available. Our key idea is to use freely moving robots equiped with microphones and loudspeakers, playing recorded utterances from existing (already annotated) speech datasets. We give an overview of previous data collection efforts and the trade-offs they make, and describe the benefits of using our robot-based approach. We finally explain the use of this method to collect room impulse response measurement.
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Jonathan Le Roux, Emmanuel Vincent, John R. Hershey, Daniel P.W. Ellis. MICbots: collecting large realistic datasets for speech and audio research using mobile robots. IEEE 2015 International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. ⟨hal-01116822⟩

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