# Improving Reverberant Speech Training Using Diffuse Acoustic Simulation

## TL;DR

This paper introduces a novel diffuse acoustic simulation method for generating realistic training data, significantly improving speech recognition and keyword spotting performance in far-field scenarios without real data fine-tuning.

## Contribution

The paper presents a physically-based acoustic simulation approach that models complex sound reflections, surpassing classical methods and enhancing training data quality for speech tasks.

## Key findings

- Speech recognition accuracy improved by 1.58%.
- Keyword spotting accuracy improved by 21%.
- Synthetic data effectively replaces real impulse responses.

## Abstract

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.