A maximum a Posterior-based reconstruction approach to speech bandwidth expansion in noise

We propose a novel bandwidth expansion algorithm for extending narrowband speech signal to wideband by exploiting segment examples pre-stored in a speaker independent database. Both narrowband and wideband representation of speech signals are pre-stored in the corpus and they are dynamically chopped into variable length segments. Narrowband segments are used dynamically to explain a given narrowband input sentence while the wideband expanded version of the input sentence is constructed correspondingly. The matching process in the narrowband favors a longer segment patch by the chosen Maximum A Posterior (MAP) criterion. As a result, the multiple choices in matching process are significantly reduced with the MAP criterion in decoding. The approach is further generalized to deal with noise corrupted narrowband input signals and the well-known Vector Taylor Series (VTS) noise adaptation algorithm is incorporated into the matching and bandwidth expansion process. A series of experiments is performed to validate the approach on both clean and noise corrupted narrowband speech where both car noise and babble noise corrupted samples are tested.