Continuous speech recognition method
First Claim
1. 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 ofrepeatedly evaluating 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, 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 spectrum frames 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, andidentifying a candidate keyword template when said selected multi-frame patterns correspond respectively to the target patterns of a said keyword template.
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Abstract
A speech recognition method for detecting and recognizing one or more keywords in a continuous audio signal is disclosed. Each keyword is represented by a keyword template representing a plurality of target patterns, and each target pattern comprises statistics of each of a plurality of spectra selected from plural short-term spectra generated according to a predetermined system for processing of the incoming audio. The spectra are processed to enhance the separation between the spectral pattern classes during later analysis. The processed audio spectra are grouped into multi-frame spectral patterns and are compared by means of likelihood statistics with the target patterns of the keyword templates. A concatenation technique employing a loosely set detection threshold makes it very unlikely that a correct pattern will be rejected.
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Citations
10 Claims
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1. 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
repeatedly evaluating 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, 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 spectrum frames 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 a candidate keyword template when said selected multi-frame patterns correspond respectively to the target patterns of a said keyword template.
Specification