We consider the task of speech recognition with loud music background interference.
We use model-based music-speech separation and train GMM models for music on the
audio prior to speech. We show over 8% relative improvement in WER at 10 dB SNR for
a real world Voice Search ASR system. We investigate the relationship between ASR
accuracy and the amount of music background used as prologue and the the size of
music models. Our study shows that performance peaks when using a music prologue of
around 6 seconds to train the music model. We hypothesize that this is due to the
dynamic nature of music and the structure of popular music. Adding more history
beyond a certain point does not improve results. Additionally, we show moderately
sized 8-component music GMM models suffice to model this amount of music prologue.
Index Terms— ASR, noise robustness, noise reduction, non-stationary noise, music