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This is a report of our lessons learned building acoustic models from 1Million hours of unlabeled speech, while labeled speech is restricted to 7,000hours. We employ student/teacher training on unlabeled data, helping scale outtarget generation in comparison to confidence model based methods, whichrequire a decoder and a confidence model. To optimize storage and toparallelize target generation, we store high valued logits from the teachermodel. Introducing the notion of scheduled learning, we interleave learning onunlabeled and labeled data. To scale distributed training across a large numberof GPUs, we use BMUF with 64 GPUs, while performing sequence training only onlabeled data with gradient threshold compression SGD using 16 GPUs. Ourexperiments show that extremely large amounts of data are indeed useful; withlittle hyper-parameter tuning, we obtain relative WER improvements in the 10 to20% range, with higher gains in noisier conditions.

Lessons from Building Acoustic Models with a Million Hours of Speech