Noise compensation in speech recognition

A method for use in sound recognition comprising the steps of:

• deriving a plurality of input signals during recognition, each of which is representative of a signal level in a corresponding region in a frequency spectrum, said frequency spectrum being one in which frequency components appear when sounds to be recognized occur;
• storing a plurality of groups of values representing respective probability density functions indicating likelihoods that input signals arise from states in finite state machine models of groups of sounds to be recognized;
• estimating an input noise level in each of said regions of said frequency spectrum;
• and
• recognizing sounds from the input signals;
• employing respective distance measures, each derived from one of said input signals and one of the said probability density functions as represented by one group of the said values, each distance measure representing, in a first circumstance, a likelihood, and, in a second circumstance, a cumulative likelihood which is cumulative from minus infinity to an upper limit, of obtaining the input signal from which that distance measure is derived from the probability function from which it is also derived,
• the first circumstance arising when the input signal from which the distance measure is derived is above a predetermined level equal to said upper limit and set substantially at to the estimated noise level in said region corresponding to that input signal;
• and
• the second circumstance arising when the input signal from which the distance measure is derived is below said predetermined level.

A method of training a sound recognition system comprising:

• deriving a plurality of groups of input signals from repetitions of nominally a same sound, each said group being representative of signal levels in a corresponding region in a frequency spectrum in which frequency components appear when sounds are to be recognized;
• estimating noise levels in each of said regions of the frequency spectrum;
• selecting only those of said input signals in each of said groups of input signals which represent signal levels above the estimated noise level in said corresponding regions;
• and
• deriving, from the selected input signals obtained from input signals above noise levels only, a plurality of groups of values representing respective substantially whole probability density functions, the probability density functions indicating likelihoods that input signals arise from states in finite state machine models for a vocabulary of groups of sounds to be recognized.