In corpus-based speech synthesis, the quality of the synthetic speech critically depends on the speech corpus. Since the high vowel in Japanese might be devoiced in the real speech, we should detect and transcribe them automatically in the corpus construction. In this paper, we apply the HMM-based method, and adopt two kinds of likelihood differences as voicing measures for different focuses. To improve the detection performance, the discriminative training is applied to voiced/ devoiced HMM training. Moreover, some features that can discriminate the voiced/devoiced units, including duration, energy and autocorrelation, are incorporated together with the likelihood differences in several methods. The experiments show different results for each high vowel, i.e. the devoicing is vowel dependent. For the vowel /i/, the discriminative training can improve the detection performance to a certain degree. And by cumulating the voicing features and the likelihood differences with optimized weights, the detection accuracy is improved. But for the vowel /u/, there is very limited improvement, even with the voicing features.