Abstract: A speech recognition method and apparatus for detecting and recognizing one or more keywords in a continuous audio signal are disclosed. Each keyword is represented by a keyword template which corresponds to a sequence of plural target patterns, and each target pattern comprises statistics representing each of a plurality of spectra selected from plural short-term spectra generated according to a predetermined system for processing the incoming audio. The target patterns also have associated therewith minimum and maximum dwell times. The dwell time is the time interval during which a given target pattern can be said to match incoming frame patterns. 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 each multi-frame spectral pattern is compared by means of likelihood statistics with the target patterns of keyword templates.

Abstract: A speech recognition method and apparatus for recognizing word strings in a continuous audio signal are disclosed. The word strings are made up of a plurality of elements, and each element, generally a word, is represented by an element template defined by a plurality of target patterns. Each target pattern is represented by a plurality of statistics describing the expected behavior of a group of spectra selected from plural short-term spectra generated by processing of the incoming audio. Each target pattern has associated therewith at least one required dwell time position and at least one optional dwell time position. The number of required dwell time positions and the sum of the required and optional dwell time positions define, in effect, the limits of a time interval during which a given target pattern can be said to match an incoming sequence of frame patterns. The incoming audio spectra are processed to enhance the separation between the spectral pattern classes during later analysis.

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 one or more target patterns, and each target pattern comprises statistics of each of at least one spectrum selected from plural short-term spectra generated according to a predetermined system for processing of the incoming audio. The incoming audio spectra are compared with the target patterns of the keyword templates and candidate keywords are selected according to a predetermined decision process. In post-decision processing, concatentation techniques, based upon a likelihood ratio test, for rejecting false alarms are disclosed. Post-decision processing can include also a prosodic test to enhance the effectiveness of the recognition apparatus.

Abstract: A computing apparatus having at least three buses and a plurality of elementary function modules in circuit connection therewith, provides increased execution speed by implementing a pipeline effect. Each module is connected to at least one of the buses and at least one of the modules is connected to at least three of the buses. The buses each comprise a plurality of individual lines organized into groups: a group of source address lines, a group of destination address lines, and a group of data carrying lines. A control element is connected to each of the buses for directing the operation of the apparatus and the control element places source and destination addresses on the bus source address and destination address lines respectively for effectively connecting or configuring the function modules according to a selected program controlled configuration. The apparatus is useful in carrying out a plurality of machine operations during a single machine instruction cycle.

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 one or more target patterns, and each target pattern comprises statistics of each of at least one spectrum selected from plural short-term spectra generated according to a predetermined system for processing of the incoming audio. The spectra are processed by a frequency equalization and normalizing method to enhance the separation between the spectral pattern classes during later analysis. The processed audio spectra are grouped into spectral patterns, are transformed to reduce dimensionality of the 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.

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.

Abstract: In the speech recognition apparatus disclosed herein, an audio signal is digitized and a succession of short-term power spectra are generated over a time interval corresponding to a spoken word. The short-term power spectra are frequency band equalized as a function of the peak amplitude occurring in each frequency band over the word interval. The changes in amplitude in each frequency band subjective time and then a limited number of frequency band equalized spectra are selected as representing equal intervals of subjective time so as to suppress variations in rate of articulation. The selected spectra are then non-linearly sealed in amplitude and transformed so as to maximize the separation between phonetically different sounds. By means of a maximum-likelihood method, the transformed selected spectra are compared with a data base representing a vocabulary to be recognized.