Bandwidth extension of narrowband speech based on blind model adaptation

Traditional telephone transmission network has speech frequency upper-limit below 4 kHz. The narrowband telephone speech (0-4 kHz) sounds muffled as compared with the original wideband speech (0-8 kHz). Artificial bandwidth extension is an economical way of enhancing the quality of narrowband speech without modifying the infrastructure of the network. Existing bandwidth extension methods usually include off-line learning phase and on-line enhancing phase. The performance of these systems depends largely on the consistency of wideband training data and actual narrowband input data. In real situation, input speeches usually mismatch with off-line training speeches, leading to serious model errors. To avoid the data mismatch, we propose a method based on blind adaptation of linear dynamic model. The benefit of our method is the exclusion of off-line training phase and experiment results show that our systems is comparable with those data-oriented systems in the measurements of highband spectral distortion. When data mismatch occurs, our system outperforms those systems.