Generation Of Large-Scale Simulated Utterances In Virtual Rooms To Train Deep-Neural Networks For Far-Field Speech Recognition In Google Home

We describe the structure and application of an acoustic room simulator to generate large-scale simulated data for training deep neural networks for far-field speech recognition. The system simulates millions of different room dimensions, a wide distribution of reverberation time and signal-to-noise ratios, and a range of microphone and sound source locations. We start with a relatively clean training set as the source and artificially create simulated data by randomly sampling a noise configuration for every new training example. As a result, the acoustic model is trained using examples that are virtually never repeated. We evaluate performance of this approach based on room simulation using a factored complex Fast Fourier Transform (CFFT) acoustic model introduced in our earlier work, which uses CFFT layers and LSTM AMs for joint multi-channel processing and acoustic modeling. Results show that the simulator-driven approach is quite effective in obtaining large improvements not only in simulated test conditions, but also in real / rerecorded conditions. This room simulation system has been employed in training acoustic models including the ones for the recently released Google Home.