The use of cognitive processing strategies and linguistic cues for efficient automatic language identification

Automatic language identification is clearly an area of research which requires much interdisciplinary effort. However, the major part of the research is carried out by the speech recognition/signal processing community where automatic language identification is seen as being related to speaker independent speech recognition and speaker identification. In particular, speaker identification methods appear to outperform all other methods and the incorporation of prosodic information has contributed only marginally to their success. This is a counterintuitive result that ignores the wealth of data made available by both the linguistic and psycholinguistic communities. The current work presents a methodology based on the cognitive processing strategies which human beings use. In particular, it is proposed that correctly parameterized, prosodic information, such as might be associated with a language with which the listener has little familiarity, is an important discriminating feature. In addition, the hierarchical approach to discrimination which people use, together with suitably parameterized linguistic cues would be more computationally efficient than the indiscriminate extraction and analysis of statistical parameters. In order to illustrate the approach a data set was chosen such as to incorporate examples of what appear to native English speakers to be two quite similar languages and one that is very different from both: Spanish, Portuguese and Chinese. The Hurst exponent is used as a novel parameterization of the prosodic signature resulting from intonation effects over a phrase or short sentence. The results of the illustrative study are in keeping with our own abilities to discriminate between those languages. They indicate that the perception of global, prosodic information subsumes the effect of similarities which may exist between the languages at the local level of acoustic features.