Concordancing for parallel spoken language corpora

Concordancing is one of the oldest corpus analysis tools, especially for written corpora. In NLP concordancing appears in training of speech-recognition system. Additionally, comparative studies of different languages result in parallel corpora. Concordancing for these corpora in a NLP context is a new approach. We propose to combine these fields of interest for a multi-purpose concordance for Spoken Language Data, opening the opportunity of combining corpus-linguistic and NLP methods resulting in a broader empirical basis for NLP research. Theoretic models for audio-concordances are discussed. Principles of the structure and design of a parallel audio concordance are given, coding by means of XML to ensure reusability and flexibility, using time stamps for referencing from annotations to the signal.