Patent ID: 8909527

Claim:
A method comprising: separating training data into speaker specific segments; performing, for a speaker specific segment of the speaker specific segments: generating spectral data representative of the speaker specific segment, the spectral data comprising a plurality of warping factors; selecting a first warping factor as a best warping factor from the plurality of warping factors based on a determination made during speech recognition of the speaker specific segment, and generating a warped spectral data representation of the spectral data using the first warping factor; comparing the warped spectral data representation to a vocal tract length normalized acoustic model; iteratively carrying out, until a comparison indicates a warping factor difference below 0.02, the acts of: selecting an other warping factor and generating an other warped spectral data representation; comparing the other warped spectral data representation to the vocal tract length normalized acoustic model, to yield the comparison; and when the other warping factor produces a closer match to the vocal tract length normalized acoustic model, saving the other warping factor as a best warping factor for the speaker specific segment; training a new acoustic model using a warped spectral data representation of all the training data that is generated using the best warping factor for each of the speaker specific segments; selecting the new acoustic model as the vocal tract length normalized acoustic model; and repeating the steps of performing and selecting until the best warping factor for each of the speaker specific segments is stable.