Unsupervised cross-lingual speaker adaptation for accented speech recognition

In this paper we present investigations on how the acoustic models in automatic speech recognition can be adapted across languages in unsupervised fashion to improve recognition of speech with a foreign accent. Recognition systems were trained on large Finnish and English corpora, and tested both on monolingual and bilingual material. Adaptation with bilingual and monolingual recognisers was compared. We found out that recognition of foreign accented English with help of Finnish adaptation training data from the same speaker was not improved significantly. However, the recognition of native Finnish using foreign accented English adaptation data was improved significantly.