In this work, we propose lattice-free MMI (LFMMI) for supervised adaptation of self-supervised pretrained acoustic model. We pretrain a Transformer model on thousand hours of untranscribed Librispeech data followed by supervised adaptation with LFMMI on three different datasets. Our results show that fine-tuning with LFMMI, we consistently obtain relative WER improvements of 10% and 35.3% on the clean and other test sets of Librispeech (100h), 10.8% on Switchboard (300h), and 4.3% on Swahili (38h) and 4.4% on Tagalog (84h) compared to the baseline trained only with supervised data.

中文翻译：

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自我监督的预训练声学模型的无格MMI适应

在这项工作中，我们提出了无格MMI（LFMMI）用于自我监督的预训练声学模型的监督适应。我们在数千小时的未转录Librispeech数据上对Transformer模型进行了预训练，然后在三个不同的数据集上使用LFMMI进行了有监督的自适应。我们的结果表明，通过LFMMI进行微调，我们在Librispeech（100h）的干净测试集和其他测试集上始终获得了10％和35.3％的相对WER改善，在Switchboard（300h）上的10.8％和Swahili（38h）的4.3％ ）和他加禄语（84h）上的4.4％，而仅使用监督数据进行训练的基线相比。