| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程學系 === 107 === More recently, a novel objective function of discriminative acoustic model training, namely Lattice-free maximum mutual information (LF-MMI), has been proposed and achieved the new state-of-the-art in automatic speech recognition (ASR). Although LF-MMI shows excellent performance in various ASR tasks with supervised training settings, its performance is often significantly degraded when with semi-supervised settings. This is because LF-MMI shares a common deficiency of discriminative training criteria, being sensitive to the accuracy of the corresponding transcripts of training utterances. In view of the above, this thesis explores two questions to LF-MMI with a semi-supervised training setting: the first one is how to improve the seed model and the second one is how to use untranscribed training data. For the former, we investigate several transfer learning approaches (e.g. weight transfer and multitask learning) and the model combination (e.g. hypothesis-level combination and frame-level combination). The distinction between the above two methods is whether extra training data is being used or not. On the other hand, for the second question, we introduce negative conditional entropy (NCE) and lattice for supervision, in conjunction with the LF-MMI objective function. A series of experiments were conducted on the Augmented Multi-Party Interaction (AMI) benchmark corpus. The experimental results show that transfer learning using out-of-domain data (ODD) and model combination based on complementary diversity can effectively improve the performance of the seed model. The pairing of NCE and lattice for supervision can improve the word error rate (WER) and WER recovery rate (WRR).
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