Fast construction of speech recognition model based on sample selection strategy

In the process of building speech recognition models, accurate labeling of speech utterances is extremely time consuming and requires trained linguists. For fast building the speech recognition models in some industrial applications, we present a novel sample selection strategy that can use very few labeled speech utterances to construct the effective recognition model. The experimental results show that our approach has better performance than state-of-the-art methods and passive learning, and has enough robust to be accepted in the worse case of building speech recognition models.