Enhanced likelihood computation using regression in a speech recognition system

A method for use with a speech recognition system in modeling one or more frames of a speech signal, the method comprising the steps of:

• tagging feature vectors associated with each frame received in a training phase with best aligning gaussian distributions;
• estimating forward and backward regression coefficients for the gaussian distributions for each frame;
• computing residual error vectors from the regression coefficients for each frame;
• modeling prediction errors to form a set of gaussian models for speech associated with each frame;
• computing one or more sets of likelihood values for each frame of a speech signal received during a recognition phase, the sets of likelihood values being based, at least in part, on the set of gaussian models;
• and
• generating a final likelihood score for each frame of the speech signal, the final likelihood score being a weighted combination of each likelihood value in a set.

An article of manufacture for use with a speech recognition system in processing a current frame of a speech signal, comprising a machine readable medium containing one or more programs which when executed implement the steps of:

• computing a likelihood value for the current frame of the speech signal;
• computing a likelihood value for at least one neighboring frame, the likelihood value of the neighboring frame including a likelihood value for at least one frame preceding and a likelihood value for at least one frame succeeding the current frame of the speech signal;
• and
• combining the likelihood values for the current and neighboring frames to form a final likelihood value for assignment in association with the current frame of the speech signal, wherein at least one of the likelihood values is assigned a corresponding weight before being combined.