Rapid Acoustic Model Adaptation Using Inverse MLLR-based Feature Generation

We propose a technique for generating a large amount of target speaker-like speech features by converting a large amount of prepared speech features of many speakers into features similar to those of the target speaker using a transformation matrix. To generate a large amount of target speaker-like features, the system only needs a very small amount of the target speaker’s utterances. This technique enables the system to adapt the acoustic model efficiently from a small amount of the target speaker’s utterances. To evaluate the proposed method, we prepared 100 reference speakers and 12 target (test) speakers. We conducted the experiments in an isolated word recognition task using a speech database collected by real PC-based distributed environments and compared our proposed method with MLLR, MAP and the method theoretically equivalent to the SAT. Experimental results proved that the proposed method needed a significantly smaller amount of the target speaker’s utterances than conventional MLLR, MAP and SAT.