Bandwidth mismatch compensation for robust speech recognition

In this paper, an iterative bandwidth mismatch compensation (BMC) algorithm is proposed to alleviate the need of multiple pre-trained models for recognizing different bandwidth speech. The BMC uses the concept of the bandwidth extension as similar as in the speech enhancement approaches. However, it aims at directly improving the recognition accuracy instead of speech intelligence or quality and utilizes only recognizer’s hidden Markov models (HMMs) for both bandwidth mismatch compensation and recognition. The BMC first detects the bandwidth of the input speech signal based on a divergence measurement. The HMM/Gaussian mixture model (GMM)based method is then used to iteratively segment the input speech utterance and compensates the speech features. Experiments on serious bandwidth mismatched conditions, i.e., training on 8 kHz and testing on 4 kHz or 5.5 kHz bandwidth database have verified the effectiveness of the proposed approach.