Investigations on exemplar-based features for speech recognition towards thousands of hours of unsupervised, noisy data

The acoustic models in state-of-the-art speech recognition systems are based on phones in context that are represented by hidden Markov models. This modeling approach may be limited in that it is hard to incorporate long-span acoustic context. Exemplar-based approaches are an attractive alter-native, in particular if massive data and computational power are available. Yet, most of the data at Google are unsupervised and noisy. This paper investigates an exemplar-based approach under this yet not well understood data regime. A log-linear rescoring framework is used to combine the exemplar-based features on the word level with the first-pass model. This approach guarantees at least baseline performance and focuses on the refined modeling of words with sufficient data. Experimental results for the Voice Search and the YouTube tasks are presented.