  We present an efficient and realistic geometric acoustic simulation approach
for generating and augmenting training data in speech-related machine learning
tasks. Our physically-based acoustic simulation method is capable of modeling
occlusion, specular and diffuse reflections of sound in complicated acoustic
environments, whereas the classical image method can only model specular
reflections in simple room settings. We show that by using our synthetic
training data, the same neural networks gain significant performance
improvement on real test sets in far-field speech recognition by 1.58% and
keyword spotting by 21%, without fine-tuning using real impulse responses.
