A syllable-scale framework for language identification

Whilst several examples of segment based approaches to language identification (LID) have been published, they have been typically conducted using only a small number of languages, or varying feature sets, thus making it difficult to determine how the segment length influences the accuracy of LID systems. In this study, phone-triplets are used as crude approximates for a syllable-length sub-word segmental unit. The proposed pseudo-syllabic length framework is subsequently used for both qualitative and quantitative examination of the contributions made by acoustic, phonotactic and prosodic information sources, and trialled in accordance with the NIST 1996 LID protocol. Firstly, a series of experimental comparisons are conducted which examine the utility of using segmental units for modelling short term acoustic features. These include comparisons between language specific Gaussian mixture models (GMMs), language specific GMMs for each segmental unit, and finally language specific hidden Markov models (HMM) for each segment, undertaken in an attempt to better model the temporal evolution of acoustic features. In a second tier of experiments, the contribution of both broad and fine class phonotactic information, when considered over an extended time frame, is contrasted with an implementation of the currently popular parallel phone recognition language modelling (PPRLM) technique. Results indicate that this information can be used to complement existing PPRLM systems to obtain improved performance. The pseudo-syllabic framework is also used to model prosodic dynamics and compared to an implemented version of a recently published system, achieving comparable levels of performance. 2005 Elsevier Ltd. All rights reserved. 0885-2308/$ see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.csl.2005.07.004 * Corresponding author. Tel.: +61 7 38641414. E-mail addresses: [email protected] (T. Martin), [email protected] (B. Baker), [email protected] (E. Wong), [email protected] (S. Sridharan). T. Martin et al. / Computer Speech and Language 20 (2006) 276–302 277