Noise compensation in speech recognition apparatus

Claim 1

A method of compensating for noisy input speech in order to improve the recognition result of a speech recognition apparatus having an input for unknown speech, converting means for converting the unknown speech into time-sampled frames of speech signals representing its spectral distribution over a given range of frequencies, storing means for storing templates of known speech in the form of speech signals representing its spectral distribution over the given range of frequencies, computing means for computing the minimum mean square error of the Euclidean squared distance between the speech signals of the unknown speech compared with the speech signals of the template speech, and recognizer means for producing a recognition result based upon the minimum mean square error computed by the computing means, wherein said method of compensating for noisy input speech comprises the following steps for producing an improved minimum mean square error estimate conditioned by compensatory characteristics of the noisy input speech: (a) computing optimal estimated distance values over the given range of frequencies for noise-free template speech, based upon comparing known speech segments, which are input in a noise-free environment and converted into corresponding templates of known speech signals t s , with unknown speech segments, which are input in a noise-free environment and converted to unknown speech signals u s ; (b) computing estimated variance values corresponding to the optimal estimated distance values for a sample population of noise-free speech segments; (c) storing said optimal estimated distance values and variance values on a look-up table associated with the template speech; (d) computing squared distance values over the given range of frequencies for input noisy unknown speech signals u s+n compared with signals t s+n representing template speech to which a spectral representation of noise n in the actual input environment is added; (e) replacing the computed squared distance values for the unknown speech signals with conditional expected distance values calculated using the optimal estimated distance values and variance values obtained from the look-up table, in order to derive noise-immune metric values for the unknown speech signals; and (f) computing the minimum mean square error of the noise-immune metric values for the unknown speech signals compared with the noise-free template speech signals, whereby an improved recognition result is obtained.

Claim 2

A method of compensating for noisy input speech in order to improve the recognition result of a speech recognition apparatus having an input for unknown speech, converting means for converting the unknown speech into time-sampled frames of speech signals representing its spectral distribution over a given range of frequencies, storing means for storing templates of known speech in the form of speech signals representing its spectral distribution over the given range of frequencies, computing means for computing the minimum mean square error of the Euclidean squared distance between the speech signals of the unknown speech compared with the speech signals of the template speech, and recognizer means for producing a recognition result based upon the minimum mean square error computed by the computing means, wherein said method of compensating for noisy input speech comprises the following steps for producing an improved minimum mean square error estimate conditioned by compensatory characteristics of the noisy input speech: (a) computing optimal estimated distance values over the given range of frequencies for noise-free template speech, based upon comparing known speech segments, which are input in a noise-free environment and converted into corresponding templates of known speech signals t s , with unknown speech segments, which are input in a noise-free environment and converted to unknown speech signals u s ; (b) computing estimated variance values corresponding to the optimal estimated distance values for a sample population of noise-free speech segments; (c) storing said optimal estimated distance values and variance values on a look-up table associated with the template speech; (d) computing squared distance values over the given range of frequencies for input noisy unknown speech signals u s+n compared with signals t s+n representing template speech to which a spectral representation of noise n in the actual input environment is added; (e) replacing the computed squared distance values for the unknown speech signals with conditional expected distance values calculated using the optimal estimated distance values and variance values obtained from the look-up table, in order to derive noise-immune metric values for the unknown speech signals; and (f) computing the minimum mean square error of the noise-immune metric values for the unknown speech signals compared with the noise-free template speech signals, whereby an improved recognition result is obtained.