In recent evaluations of automatic language recognition systems, phonotactic approaches have proven highly effective . However, as most of these systems rely on underlying ASR techniques to derive a phonetic tokenization, these techniques are potentially susceptible to acoustic variability from non-language sources (i.e. gender, speaker, channel, etc.). In this paper we apply techniques from ASR research to normalize and adapt HMM-based phonetic models to improve phonotactic language recognition performance. Experiments we conducted with these techniques show an EER reduction of 29% over traditional PRLM-based approaches.
Bibliographic reference. Shen, Wade / Reynolds, Douglas (2007): "Improving phonotactic language recognition with acoustic adaptation", In INTERSPEECH-2007, 358-361.