Identifying the language of an unknown speech signal consists of evaluating the observation probability of the signal for each of the model sets corresponding to the languages of interest, and proposing the identity associated with the model set having the highest probability. The latter approach requires less development effort to setup and is easier to adapt to a new language. A common phone set was obtained using an agglomerative clustering algorithm with all the phone models of the different languages. The automatically derived clusters for the most part correspond to linguistically similar or identical units. The statistical approach adopted for acoustic-phonetic modeling requires large speech corpora for accurate parameter estimation.
Data collection remains an important support activity for our research.