Abstract: A speech recognition system adaptively estimates a warping factor used to reduce speaker variability. The warping factor is estimated using a small window (e.g. 100 ms) of speech. The warping factor is adaptively adjusted as more speech is obtained until the warping factor converges or a pre-defined maximum number of adaptation is reached. The speaker may be placed into a group selected from two or more groups based on characteristics that are associated with the speaker's window of speech. Different step sizes may be used within the different groups when estimating the warping factor. VTLN is applied to the speech input using the estimated warping factor. A linear transformation, including a bias term, may also be computed to assist in normalizing the speech along with the application of the VTLN.

Abstract: A speech recognition system adaptively estimates a warping factor used to reduce speaker variability. The warping factor is estimated using a small window (e.g. 100 ms) of speech. The warping factor is adaptively adjusted as more speech is obtained until the warping factor converges or a pre-defined maximum number of adaptation is reached. The speaker may be placed into a group selected from two or more groups based on characteristics that are associated with the speaker's window of speech. Different step sizes may be used within the different groups when estimating the warping factor. VTLN is applied to the speech input using the estimated warping factor. A linear transformation, including a bias term, may also be computed to assist in normalizing the speech along with the application of the VTLN.

Abstract: Described is a technology by which a deep-structured (multiple layered) conditional random field model is trained and used for classification of sequential data. Sequential data is processed at each layer, from the lowest layer to a final (highest) layer, to output data in the form of conditional probabilities of classes given the sequential input data. Each higher layer inputs the conditional probability data and the sequential data jointly to output further probability data, and so forth, until the final layer which outputs the classification data. Also described is layer-by-layer training, supervised or unsupervised. Unsupervised training may process raw features to minimize average frame-level conditional entropy while maximizing state occupation entropy, or to minimize reconstruction error.

Abstract: Described is a technology by which a deep-structured (multiple layered) conditional random field model is trained and used for classification of sequential data. Sequential data is processed at each layer, from the lowest layer to a final (highest) layer, to output data in the form of conditional probabilities of classes given the sequential input data. Each higher layer inputs the conditional probability data and the sequential data jointly to output further probability data, and so forth, until the final layer which outputs the classification data. Also described is layer-by-layer training, supervised or unsupervised. Unsupervised training may process raw features to minimize average frame-level conditional entropy while maximizing state occupation entropy, or to minimize reconstruction error.