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• W1559023568 abstract "The standard training approach for a hidden Markov model (HMM) based speech recognition system uses an expectation maximization (EM) based supervised training framework to estimate parameters. EM-based parameter estimation for speech recognition is performed using several complicated stages of iterative reestimation. These stages are heuristic in nature and prone to human error. This paper describes a new training recipe that reduces the complexity of the training process, while retaining the robustness of the EM-based supervised training framework. This paper show that the network training recipe can achieve comparable recognition performance to a traditional trainer while alleviating the need for complicated systems and training recipes for spoken language processing systems." @default.
• W1559023568 created "2016-06-24" @default.
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• W1559023568 date "2004-09-01" @default.
• W1559023568 modified "2023-09-24" @default.
• W1559023568 title "Network training for continuous speech recognition" @default.
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