Abstract: A technique to improve the recognition accuracy when transcribing speech data that contains data from a wide range of environments. Input data in many situations contains data from a variety of sources in different environments. Such classes include: clean speech, speech corrupted by noise (e.g., music), non-speech (e.g., pure music with no speech), telephone speech, and the identity of a speaker. A technique is described whereby the different classes of data are first automatically identified, and then each class is transcribed by a system that is made specifically for it. The invention also describes a segmentation algorithm that is based on making up an acoustic model that characterizes the data in each class, and then using a dynamic programming algorithm (the viterbi algorithm) to automatically identify segments that belong to each class. The acoustic models are made in a certain feature space, and the invention also describes different feature spaces for use with different classes.

Abstract: A method and apparatus for removing the effect of background music or noise from speech input to a speech recognizer so as to improve recognition accuracy has been devised. Samples of pure music or noise related to the background music or noise that corrupts the speech input are utilized to reduce the effect of the background in speech recognition. The pure music and noise samples can be obtained in a variety of ways. The music or noise corrupted speech input is segmented in overlapping segments and is then processed in two phases: first, the best matching pure music or noise segment is aligned with each speech segment; then a linear filter is built for each segment to remove the effect of background music or noise from the speech input and the overlapping segments are averaged to improve the signal to noise ratio. The resulting acoustic output can then be fed to a speech recognizer.