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Minimizing empirical error training and adaptation of statistical language models and context free grammar in automatic speech recognition
Minimizing empirical error training and adaptation of statistical language models and context free grammar in automatic speech recognition
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机译:在自动语音识别中最小化经验错误训练以及统计语言模型和上下文无关语法的适应
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摘要
Architecture for minimizing an empirical error rate by discriminative adaptation of a statistical language model in a dictation and/or dialog application. The architecture allows assignment of an improved weighting value to each term or phrase to reduce empirical error. Empirical errors are minimized whether a user provides correction results or not based on criteria for discriminatively adapting the user language model (LM)/context-free grammar (CFG) to the target. Moreover, algorithms are provided for the training and adaptation processes of LM/CFG parameters for criteria optimization.
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