Towards Exploiting the Potential of Environment Adaptation

The offline HMM adaptation of a generic car speech recognizer to a specific car environment is investigated. For the generation of the adaptation database the approach of environment adapted databases (EADB) is applied that avoids real speech recordings in the target environment and therefore reduces the effort significantly. With MLLR adaptation using such an EADB a relative reduction of the word error rate of more than 10% can be achieved on a British city names task. It is proven by adaptation on real speech recordings from the target environment that the improvement with EADBs fully exploits the potential of HMM adaptation for the given car. Additionally it can be shown that if task matching material is available for adaptation a performance improvement of more than 30% can be reached with an additional maximum likelihood training iteration