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Confidence estimation for speech recognition systems using conditional random fields trained with partially annotated data

Conditional random fields (CRF) can generate high-quality confidence measure scores (CMS) for speech recognition systems. However, like many other real-world machine learning tasks, there are only limited annotated data for training but always abundant unlabeled data, which requires too much human efforts and expertise to annotate. To address this issue, we use a scheme of CRF training for ASR confidence estimation, which does not require full annotation transcripts but exploits partially annotated data. We use multiple ASR systems and other freely accessible resources (e.g. caption texts) to generate partially annotated data. Compared with only using a small amount of annotated data and totally using automatically generated unfaithful annotations, the CMS can be enhanced by our proposed method.