Language identification using multiple knowledge sources

Language identification experiments have been carried out on language pairs taken from seven of the languages in the OGI Multi-language Telephone Speech Corpus. This builds on previous work but introduces new techniques which are used to exploit the acoustic and phonetic differences between the languages. Subword hidden Markov models for the pair of languages are matched to unknown utterances resulting in three measures: the acoustic match, the phoneme frequencies and frequency histograms. Each of these measures gives 80 to 90% accuracy in discriminating language pairs. However these multiple knowledge sources are also combined to give improved results. Majority decision, logistic regression and a linear classifier were compared as data fusion techniques. The linear classifier performed the best giving an average accuracy of 89 to 93% on the pairs from the seven languages.