A sung lyrics error detection system is proposed to examine if the lyrics sung by a singer are incorrect, thereby providing a clue for singing skill evaluation. In essence, sung lyrics error detection is similar to the problem of speech utterance verification in the speech recognition research community, and therefore the techniques in the latter can be applied to the former. However, our experiment found that a speech utterance verification system is far from capable of handling singing data, mainly because of the significant difference between singing and speech. To tackle this problem, we develop two strategies, respectively, from a signal processing perspective and from a model processing perspective. In the signal processing perspective, we recognize that the vowels are often lengthened during singing, and thus propose vowel shrinking/decimation to adjust the length of a vowel in singing to a normal length in speaking. In the model processing perspective, we combine a duration modeling concept into the acoustic modeling to reduce the differences between singing and speech. Our experiments show that the proposed methods can improve the performance of the sung lyrics error detection noticeably, compared to a baseline system based on speech utterance verification.