  The paper presents methods for evaluating the accuracy of alignments between
transcriptions and audio recordings. The methods have been applied to the
Spoken British National Corpus, which is an extensive and varied corpus of
natural unscripted speech. Early results show good agreement with human ratings
of alignment accuracy. The methods also provide an indication of the location
of likely alignment problems; this should allow efficient manual examination of
large corpora. Automatic checking of such alignments is crucial when analysing
any very large corpus, since even the best current speech alignment systems
will occasionally make serious errors. The methods described here use a hybrid
approach based on statistics of the speech signal itself, statistics of the
labels being evaluated, and statistics linking the two.
