Abstract: Audio signals from any array of microphones are individually filtered, delayed, and scaled in order to form an acoustic beam that focuses the array on a particular region. Nonlinear robust signal estimation processing is applied to the resulting set of audio signals to generate an output signal for the array. The nonlinear robust signal estimation processing may involve dropping or otherwise reducing the magnitude of one or more of the highest and lowest data in each set of values from the resulting audio signals and then selecting the median from or generating an average of the remaining values to produce a representative, central value for the output audio signal. The nonlinear robust signal estimation processing effectively discriminates against noise originating at an unknown location outside of the focal region of the acoustic beam.

Abstract: A sound processing arrangement couples sound from a prescribed environment through a fixed microphone array to a signal processing arrangement having a specifiable preferred sound source location. The microphone pickup signals are combined with a set of weighting signals to adjust the directional response pattern in successive analysis time intervals. The weighting signals are modified in each analysis time interval so that the total acoustic signal power of the signal processing arrangement output signal is decreased toward a minimum while substantially unity power transfer of sound signals from said preferred location is maintained at all frequencies over a prescribed frequency range. In this way, the preferred source location is in the main beam while unwanted sound source locations are at the null points of the adjusted directional response pattern.

Abstract: Markov model speech pattern templates are formed for speech analysis systems by analyzing identified speech patterns to generate frame sequences of acoustic feature signals representative thereof. The speech pattern template is produced by iteratively generating succeeding Markov model signal sets starting with an initial Markov model signal set. Each iteration includes forming a set of signals representative of the current iteration Markov model of the identified speech pattern responsive to said frame sequences of acoustic feature signals and one of the previous Markov model signal sets and comparing the current iteration Markov model signal set with said previous Markov model signal set to generate a signal corresponding to the similarity therebetween. The iterations are terminated when said similarity signal is equal to or smaller than a predetermined value and the last formed Markov model signal set is selected as a reference template for said identified speech pattern.

Abstract: A speech recognizer includes a plurality of stored constrained hidden Markov model reference templates and a set of stored signals representative of prescribed acoustic features of the said plurality of reference patterns. The Markov model template includes a set of N state signals. The number of states is preselected to be independent of the reference pattern acoustic features and preferably substantially smaller than the number of acoustic feature frames of the reference patterns. An input utterance is analyzed to form a sequence of said prescribed feature signals representative of the utterance. The utterance representative prescribed feature signal sequence is combined with the N state constrained hidden Markov model template signals to form a signal representative of the probability of the utterance being each reference pattern. The input speech pattern is identified as one of the reference patterns responsive to the probability representative signals.