Low-Frequency Compensated Synthetic Impulse Responses For Improved Far-Field Speech Recognition

Abstract

We propose a method for generating low-frequency compensated synthetic impulse responses that improve the performance of farfield speech recognition systems trained on artificially augmented datasets. We design linear-phase filters that adapt the simulated impulse responses to equalization distributions corresponding to realworld captured impulse responses. Our filtered synthetic impulse responses are then used to augment clean speech data from LibriSpeech dataset. We evaluate the performance of our method on the real-world LibriSpeech test set. In practice, our low-frequency compensated synthetic dataset can reduce the word-error-rate by up to 8.8% for far-field speech recognition.