Continuous speech recognition method

In a speech analysis system in which an audio signal is spectrum analyzed for recognizing at least one predetermined keyword in a continuous audio signal, each said keyword being characterized by a template having at least one target pattern, said target patterns having an ordered sequence and each target pattern representing a plurality of short-term power spectra spaced apart in real time, an analysis method comprising the steps of

• repeatedly evaluating electrical signals representing a set of parameters determining a short-term power spectrum of said audio signal with each of a plurality of equal duration sampling intervals, thereby to generate a continuous time ordered sequence of short-term audio power spectrum frames,
• repeatedly generating electrical signals representing a peak spectrum corresponding to said short-term power spectrum frames by a fast attack, slow decay peak detecting function,
• for each short-term power spectrum frame, dividing the amplitude of each frequency band by the corresponding intensity value in the corresponding peak spectrum, thereby to generate a frequency band equalized spectrum frame corresponding to a compensated audio signal having the same maximum short-term energy content in each of the frequency bands comprising the spectrum,
• repeatedly selecting from said sequence of equalized frames, one first frame and at least one later occurring frame to form a multi-frame pattern,
• comparing each thus formed multi-frame pattern with each first target pattern of each keyword template,
• deciding whether each said multi-frame pattern corresponds to a said first target pattern of a keyword template,
• for each multi-frame pattern which, according to said deciding step, corresponds to a said first target pattern of a potential candidate keyword, selecting later occurring short-term power spectrum equalized frames to form later occurring multiframe patterns,
• deciding whether said later occurring multi-frame patterns correspond respectively to successive target patterns of said potential candidate keyword template, and
• identifying electrical signals representing a candidate keyword template when said selected multi-frame patterns correspond respectively to the target patterns of a said keyword template.

In a pattern recognition system for identifying in a data stream electrical signals representing at least one target pattern characterized by a vector of recognition elements x.sub.i, said elements having a statistical distribution, the analysis method comprising the steps of

• determining from plural design set pattern samples x of the target pattern a covariance matrix K;
• determining from said plural design set patterns an expected value vector x;
• calculating from the covariance matrix K, a plurality of eigenvectors e.sub.i having eigenvalues v.sub.i where v.sub.i .gtoreq.v.sub.i +1;
• selecting electrical signals representing unknown patterns y from said data stream;
• transforming the electrical signals representing each pattern y into electrical signals representing a new vector (W.sub.1, W.sub.2, . . . , W.sub.p, R) where
• W.sub.i =e.sub.i (y-x),
• p is a positive integer constant less than the number of elements of the pattern y, and R is the reconstruction error statistic and equals ##EQU17## and deciding, by applying a likelihood statistic function to electrical signals representing said new vector (W.sub.1, W.sub.2, . . . , W.sub.p, R), whether said pattern y is identified with the target pattern.

In a speech analysis system for recognizing at least one predetermined keyword in a continuous audio signal, each said keyword being characterized by a template having at least one target pattern, said target patterns having an ordered sequence and each target pattern representing a plurality of short-term power spectra spaced apart in real time, an analysis method comprising the steps of

• for each target pattern, determining from electrical signals representing plural design set pattern samples x of a said target pattern having elements x.sub.i, a covariance matrix K;
• determining from said plural design set patterns an expected value vector x;
• calculating from the covariance matrix K a plurality of eigenvectors e.sub.i having eigenvalues v.sub.i where v.sub.i .gtoreq.v.sub.i +1;
• repeatedly evaluating electrical signals representing a set of parameters determining a short-term power spectrum of said audio signal within each of a plurality of equal duration sampling intervals, thereby to generate a continuous time ordered sequence of short-term audio power spectrum frames;
• repeatedly selecting from said sequence of frames, electrical signals representing one first frame and at least one later occurring frame to form a multi-frame pattern y;
• transforming the electrical signals representing each multi-frame pattern y into electrical signals representing new vectors W, represented as (W.sub.1, W.sub.2, . . ., W.sub.p, R), where
• W.sub.i =e.sub.i (y-x);
• p is a positive integer constant less than the number of elements of the pattern y, and
• R is the reconstruction error statistic and equals ##EQU20## deciding whether each said transformed pattern corresponds to a said first target pattern of a keyword template;
• for each pattern which, according to said deciding step, corresponds to a said first target pattern of a potential candidate keyword, selecting later occurring short-term power spectra to form later occurring multi-frame patterns;
• deciding whether said later occurring multi-frame patterns correspond respectively to successive target patterns of said potential candidate keyword template;
• and
• identifying electrical signals representing a candidate keyword template when said selected multi-frame patterns correspond respectively to the target patterns of a said keyword template.

In a pattern recognition system for identifying in a data stream electrical signals representing at least one target pattern characterized by a vector of recognition elements x.sub.i, said elements having a statistical distribution, the analysis method comprising the steps of

• selecting electrical signals representing unknown patterns y from said data stream;
• transforming the electrical signals representing each pattern y into electrical signals representing a new vector (W.sub.1, W.sub.2, . . . , W.sub.p, R) where
• the W.sub.i represent elements of a vector obtained in a principle component analysis,
• p is a positive integer constant less than the number of elements of the pattern y, and R is a reconstruction error statistic representing information at least in part not contained in said elements W.sub.i ;
• and
• deciding, by applying a likelihood statistic function to electrical signals representing said new vector (W.sub.1, W.sub.2, . . . , W.sub.p, R), whether said pattern y is identified with the target pattern.

In a speech analysis system for recognizing at least one predetermined keyword in a continuous audio signal, each said keyword being characterized by a template having at least one target pattern, said target patterns having an ordered sequence and each target pattern representing a plurality of short-term power spectra spaced apart in real time, an analysis method comprising the steps of

• for each target pattern, determining from electrical signals representing plural design set pattern samples x of a said target pattern and said samples x having elements x.sub.i ;
• repeatedly evaluating electrical signals representing a set of parameters determining a short-term power spectrum of said audio signal within each of a plurality of equal duration sampling intervals, thereby to generate a continuous time ordered sequence of short-term audio power spectrum frames;
• repeatedly selecting from said sequence of frames, electrical signals representing one first frame and at least one later occurring frame to form a multi-frame pattern y;
• transforming the electrical signals representing each multi-frame pattern y into electrical signals representing new vectors W, represented as (W.sub.1, W.sub.2, . . . , W.sub.p, R), where
• W.sub.i represent elements of a vector obtained in a principle component analysis,
• p is a positive integer constant less than the number of elements of the pattern y, and
• R is a reconstruction error statistic representing at least in part information not contained in said elements W.sub.i ;
• deciding whether each said transformed pattern corresponds to a said first target pattern of a keyword template;
• for each pattern which, according to said deciding step, corresponds to a said first target pattern of a potential candidate keyword, selecting later occurring short-term power spectra to form later occurring multi-frame patterns;
• deciding whether said later occurring multi-frame patterns correspond respectively to successive target patterns of said potential candidate keyword template;
• and
• identifying electrical signals representing a candidate keyword template when said selected multi-frame patterns correspond respectively to the target patterns of a said keyword template.