Investigation of using different Chinese word segmentation standards and algorithms for automatic speech recognition
In phonotactic language recognition systems, the use of acoustic model adaptation prior to phone lattice decoding has been proposed to deal with the mismatch between training and test conditions. In this paper, a novel approach using diversified phonotactic features from parallel acoustic model adaptation is proposed. Specifically, the parallel model adaptation involves independent mean-only and variance-only MLLR adaptation. A quantitative method to measure the diversity between two sets of high-dimensional phonotactic features is introduced. Our experiment shows that this novel approach achieves an EER of 3.07% in the 30-second condition of the 2007 NIST Language Recognition Evaluation (LRE) tasks. It brings a 17.3% relative improvement in EER over the baseline system using a SAT phone model and CMLLR for model adaptation.