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• W2288402171 abstract "We describe the development of our speech-to-text transcription systems for the 2015 Multi-Genre Broadcast (MGB) challenge. Key features of the systems are: a segmentation system based on deep neural networks (DNNs); the use of HTK 3.5 for building DNN-based hybrid and tandem acoustic models and the use of these models in a joint decoding framework; techniques for adaptation of DNN based acoustic models including parameterised activation function adaptation; alternative acoustic models built using Kaldi; and recurrent neural network language models (RNNLMs) and RNNLM adaptation. The same language models were used with both HTK and Kaldi acoustic models and various combined systems built. The final systems had the lowest error rates on the evaluation data." @default.
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• W2288402171 date "2015-12-01" @default.
• W2288402171 modified "2023-09-26" @default.
• W2288402171 title "Cambridge university transcription systems for the multi-genre broadcast challenge" @default.
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• W2288402171 doi "https://doi.org/10.1109/asru.2015.7404856" @default.
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