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The paper presents methods for evaluating the accuracy of alignments betweentranscriptions and audio recordings. The methods have been applied to theSpoken British National Corpus, which is an extensive and varied corpus ofnatural unscripted speech. Early results show good agreement with human ratingsof alignment accuracy. The methods also provide an indication of the locationof likely alignment problems; this should allow efficient manual examination oflarge corpora. Automatic checking of such alignments is crucial when analysingany very large corpus, since even the best current speech alignment systemswill occasionally make serious errors. The methods described here use a hybridapproach based on statistics of the speech signal itself, statistics of thelabels being evaluated, and statistics linking the two.

Detecting gross alignment errors in the Spoken British National Corpus