LATTICE-BASED UNSUPERVISED ACOUSTIC MODEL TRAINING

Unsupervised acoustic model training has been successfully used to improve the performance of automatic speech recognition systems when only a small amount of manually transcribed data is available for the target domain. The most common approach is use automatic transcriptions to guide acoustic model estimation. However, since the best recognition hypotheses are known to contain errors, we propose to consider multiple transcription hypotheses during training. The idea is that the EM process can benefit from the estimated posterior probabilities of the hypotheses to converge to a better solution. The proposed unsupervised training method is based on lattices. Lattice-based training gives a relative improvement of 2.2% over 1-best training on a Broadcast News transcription task and converges faster with the iterative incremental training.