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Main Dialect Identification in Mainland China, Hong Kong and Taiwan

机译：中国大陆，香港和台湾的主要方言识别

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As an emerging field of speech recognition, dialect identification plays an important role for promoting applications of speech recognition technology. Since the communications among Mainland China, Hong Kong and Taiwan are becoming frequently, it is particularly necessary to identify their dialects. This paper makes contributions to this issue in the following three-folds: 1) we build a speech corpus for main dialects of the three areas; 2) we use the popular GMM based method to extensively evaluate the main dialects between Mainland China and Hong Kong and the ones between Mainland China and Taiwan, and we find the differences between Mainland China Mandarin and Taiwan Mandarin are much smaller than those between Mandarin and Cantonese, resulting in unsatisfactory results in the latter case; 3) we propose an improved method based on the analysis of GMM, namely, maximum KL distance based Gaussian component selection (MKLD-GCS) in order to improve the performance of dialect identification between Mainland China Mandarin and Taiwan Mandarin. Experimental results show that our proposed method obtains better identification performance than related methods.
机译：方言识别作为语音识别的新兴领域，在促进语音识别技术的应用方面起着重要作用。由于中国内地，香港和台湾之间的交流越来越频繁，因此特别需要确定它们的方言。本文从以下三个方面为这一问题做出了贡献：1）我们为这三个地区的主要方言建立了语料库。 2）我们使用流行的基于GMM的方法广泛评估了中国大陆和香港之间以及中国大陆和台湾之间的主要方言，我们发现中国大陆普通话和台湾普通话之间的差异远小于普通话和普通话之间的差异。广东话，导致后者情况不理想； 3）我们提出了一种基于GMM分析的改进方法，即基于最大KL距离的高斯成分选择（MKLD-GCS），以提高中国大陆普通话和台湾普通话之间的方言识别性能。实验结果表明，本文提出的方法比相关方法具有更好的识别性能。

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