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Enhanced automatic speech recognition using mapping between unsupervised and supervised speech model parameters trained on same acoustic training data
Enhanced automatic speech recognition using mapping between unsupervised and supervised speech model parameters trained on same acoustic training data
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机译：使用在相同声学训练数据上训练的无监督和受监督语音模型参数之间的映射来增强自动语音识别
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摘要
Techniques for enhanced automatic speech recognition are described. An enhanced ASR system may be operative to generate an error correction function. The error correction function may represent a mapping between a supervised set of parameters and an unsupervised training set of parameters generated using a same set of acoustic training data, and apply the error correction function to an unsupervised testing set of parameters to form a corrected set of parameters used to perform speaker adaptation. Other embodiments are described and claimed.
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