In this paper, a novel approach for wide band speech generation from narrow band is proposed. The proposed method is based on Subband Hidden Markov Model (Subband HMM). To train the HMM, a set of wide band speech is divided into a number of subbands and features are extracted independently. These extracted features are recombined and HMMs are trained by EM algorithm. The training process makes HMM to model the feature of signal in a single subband. In parallel, HMM learns the corresponding feature of all other subbands. The correspondence makes it possible to estimate the unobserved frequency component using the correspondence of narrowband signal to the HMM. We further investigate the application of the proposed method to denoising. Some experimental results are shown to confirm the validity of the proposed method.
['Mitsuhiro Hosoki', 'Takayuki Nagai', 'Akira Kurematsu']
Speech signal band width extension and noise removal using subband HMN