An i-vector Based Approach to Training Data Clustering for Improved Speech Recognition

We present a new approach to clustering training data for improved speech recognition. Given a training corpus, a so-called i-vector is extracted from each training utterance. A hierarchical divisive clustering algorithm is then used to cluster the training i-vectors into multiple clusters. For each cluster, an acoustic model (AM) is trained accordingly. Such trained multiple AMs can then be used in recognition stage to improve recognition accuracy. The proposed approach is very efficient therefore can deal with very large scale training corpus on current mainstream computing platforms. We report experimental results on a voice search task with 7,500 hours of speech training data.