類神經網路訓練結合環境群集及專家混合系統於強健性語音辨識

Recently, automatic speech recognition (ASR) using neural network (NN) based acoustic model (AM) has achieved significant improvements. However, the mismatch (including speaker and speaking environment) of training and testing conditions still confines the applicability of ASR. This paper proposes a novel approach that combines the environment clustering (EC) and mixture of experts (MOE) algorithms (thus the proposed approach is termed EC-MOE) to enhance the robustness of ASR against mismatches. In the offline phase, we split the entire training set into several subsets, with each subset characterizing a specific speaker and speaking environment. Then, we use each subset of training data to prepare an NN-based AM. In the online phase, we use a Gaussian mixture model (GMM)-gate to determine the optimal output from the multiple NN-based AMs to render the final recognition results. We evaluated the proposed EC-MOE approach on the Aurora 2 continuous digital speech recognition task. Comparing to the baseline system, where only a single NN-based AM is used for recognition, the proposed approach achieves a clear word error rate (WER) reduction of 5.9 % (5.25% to 4.94%).