Abstract
This thesis presents the detection of retroflex and non-retroflex for Mandarin Chinese. The objective of our research is to determine whether an initial within a syllable obtained from forced alignment has the characteristics of a retroflex or not. The decision rule used in this paper is similar to speaker verification. Firstly, GMM-based retroflex and non-retroflex models are trained. Secondly, we adjust the threshold of the likelihood ratio to achieve the equal error rate (EER). In addition to MFCC, spectrum moments and formants are also used as our speech features. The experimental results indicate that spectrum moments and formants are able to improve the performance of the retroflex and non-retroflex detection rate. The best equal error rate obtained from our experiments is 17.69%.