Skip to Main content Skip to Navigation
Journal articles

Multi-microphone speech recognition in everyday environments

Abstract : Multi-microphone signal processing techniques have the potential to greatly improve the robustness of speech recognition (ASR) in distant microphone settings. However, in everyday environments, typified by complex non-stationary noise backgrounds, designing effective multi-microphone speech recognition systems is non trivial. In particular, optimal performance requires the tight integration of the front-end signal processing and the back-end statistical speech and noise source modelling. The best way to achieve this in a modern deep learning speech recognition framework remains unclear. Further, variability in microphone array design --- and consequent lack of real training data for any particular configuration --- may mean that systems have to be able to generalise from audio captured using mismatched microphone geometries or produced using simulation.
Document type :
Journal articles
Complete list of metadata
Contributor : Emmanuel Vincent Connect in order to contact the contributor
Submitted on : Sunday, March 5, 2017 - 11:55:18 PM
Last modification on : Wednesday, November 3, 2021 - 7:57:21 AM
Long-term archiving on: : Tuesday, June 6, 2017 - 12:24:21 PM


Files produced by the author(s)




Jon Barker, Ricard Marxer, Emmanuel Vincent, Shinji Watanabe. Multi-microphone speech recognition in everyday environments. Computer Speech and Language, Elsevier, 2017, 46, pp.386-387. ⟨10.1016/j.csl.2017.02.007⟩. ⟨hal-01483469⟩



Les métriques sont temporairement indisponibles