We apply the recently proposed Context-Dependent Deep-Neural-Network HMMs, CD-DNN-HMMs, to speech-to-text transcription. For single-pass speaker-independent recognition on the RT03S Fisher portion of phone-call transcription benchmark (Switchboard), the word-error rate is reduced from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs, to 18.5%.a 33% relative improvement.
CD-DNN-HMMs combine classic artificial-neural-network HMMs with traditional tied-state triphones and deep-belief-network pretraining. They had previously been shown to reduce errors by 16% relatively when trained on tens of hours of data using hundreds of tied states. This paper takes CD-DNN-HMMs further and applies them to transcription using over 300 hours of training data, over 9000 tied states, and up to 9 hidden layers, and demonstrates how sparseness can be exploited.
On four less well-matched transcription tasks, we observe relative error reductions of 22.28%.
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