关键词: 临近最优批量主动学习, submodular函数, 语音语料选择, 藏语拉萨话语音识别

Abstract: A large number of annotated speech corpus is needed to train speech recognition models. Tibetan language is one of Chinese ethnic minority languages, it lacks the annotator. So it is very time-consuming and costly for labeling Tibetan speech data. However, the active learning method can select a number of informative samples from unlabeled data according to the target of speech recognition to the user for annotation, in order to use a small amount of high quality training sample to build the accurate recognition models. This paper studies the method of speech data selection for Lhasa-Tibetan speech recognition based on active learning, and proposes a near-optimal batch mode objective function, and proves this objective function is submodular function. The experimental results show that the presented method can use less training data to ensure the accuracy of speech recognition model, and can reduce the workload of manual annotation.

Key words: near-optimal batch mode active learning, submodular function, speech corpus selection, Lhasa-Tibetan speech recognition