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Accurate labeling and segmentation of the unit inventory database is of vital importance to the quality of unit selection text-to-speech synthesis. Misalignments and mismatch between the predicted and pronounced unit sequences require manual correction to achieve natural sounding synthesis. In this paper we have used a log likelihood ratio based utterance verification to automatically detect annotation errors in a Norwegian two-speaker synthesis database. Each sentence is assigned a confidence score and those falling below a threshold can be discarded or manually inspected and corrected. Using equal reject number as a criterion the transcription sentence error rate was reduced from 9.8% to 2.7%. Insertions are the largest error category, and 95.6% of these were detected. A closer inspection of false rejections was performed to assess (and improve) the phoneme prediction system.