Text- and speech-based phonotactic models for spoken language identification of Basque and Spanish

Abstract

This paper presents a series of spoken language identification experiments involving Spanish and Basque. Spanish and Basque are both official languages in the Basque Country, a region located in northern Spain. We focused our research on the study of several phonotactic-based methodologies, analysing at the same time the performance of phonotactic models trained from text and speech samples and the use of phone and phone sequences as decoding units. Although we focus mainly on Spanish–Basque identification, the analysis is later extended to English, so that more generic conclusions can be drawn. From the bilingual results, we can conclude that the text-based phonotactic models can perform similarly to the audio-based ones when applied to read speech. Moreover, when using task-specific information it is also possible to achieve a high accuracy. The use of phone sequences as decoding units results, in most of the cases, in a decrease in performance and appears to be useful when constraining the phone decoders to those sequences. Similar conclusions can be drawn from the trilingual experiments.