Cross-lingual portability of MLP-based tandem features - a case study for English and Hungarian
Lászlá Tóth, Joe Frankel, Gábor Gosztolya, Simon King · 2008
One promising approach for building ASR systems for lessresourced languages is cross-lingual adaptation.Tandem ASR is particularly well suited to such adaptation, as it includes two cascaded modelling steps: feature extraction using multi-layer perceptrons (MLPs), followed by modelling using a standard HMM.The language-specific tuning can be performed by adjusting the HMM only, leaving the MLP untouched.Here we examine the portability of feature extractor MLPs between an Indo-European (English) and a Finno-Ugric (Hungarian) language.We present experiments which use both conventional phone-posterior and articulatory feature (AF) detector MLPs, both trained on a much larger quantity of (English) data than the monolingual (Hungarian) system.We find that the cross-lingual configurations achieve similar performance to the monolingual system, and that, interestingly, the AF detectors lead to slightly worse performance, despite the expectation that they should be more language-independent than phone-based MLPs.However, the cross-lingual system outperforms all other configurations when the English phone MLP is adapted on the Hungarian data.