Unsupervised and active learning in automatic speech recognition for call classification

A key challenge in rapidly building spoken natural language dialog applications is minimizing the manual effort required in transcribing and labeling speech data. This task is not only expensive but also time consuming. We present a novel approach that aims at reducing the amount of manually transcribed in-domain data required for building automatic speech recognition (ASR) models in spoken language dialog systems. Our method is based on mining relevant text from various conversational systems and Web sites. An iterative process is employed where the performance of the models can be improved through both unsupervised and active learning of the ASR models. We have evaluated the robustness of our approach on a call classification task that has been selected from AT&T VoiceTone^SM customer care. Our results indicate that with unsupervised learning it is possible to achieve a call classification performance that is only 1.5% lower than the upper bound set when using all available in-domain transcribed data.