Low resourced languages suffer from limited training data and resources. Data augmentation is a common approach to increasing the amount of training data. Additional data is synthesized by manipulating the original data with a variety of methods. Unlike most previous work that focuses on a single technique, we combine multiple, complementary augmentation approaches. The first stage adds noise and perturbs the speed of additional copies of the original audio. The data is further augmented in a second stage, where a novel fMLLR-based augmentation is applied to bottleneck features to further improve performance. A reduction in word error rate is demonstrated on four languages from the IARPA Babel program. We present an analysis exploring why these techniques are beneficial. Index Terms: speech recognition, deep neural networks, data augmentation
Speed PerturbationKo et al. [18] showed success by manipulating the speed of the data. They demonstrated a performance improvement over the more common vocal tract length perturbation (VTLP) technique [8]. Using the Sox utility [19], the original data is perturbed by a warping factor that effects both the frequencies and the duration of the speech. The speed change is accomplished by resampling the waveform, which not only changes the duration, but also scales the pitch, vocal tract length, and all spectral frequencies by the same factor. Our setup uses a randomly selected warping factor between 0.9 and 1.1 (this was also the