Raytheon Applied Signal Technology, Inc.

Raytheon Company

Jonathan C Wintrode

Nicholas J Hinnerschitz

Aleksandr R Jouravlev

Method for distributed asynchronous dependency-based content processing includes: receiving a multi-media digital content and a task to be performed on the multi-media digital content; determining types of the multi-media digital content; generating a workflow graph for each type of the content, each workflow graph including data dependency conditions of the task; generating a task message for each workflow graph; broadcasting the task messages to a respective task queue of an initial state of a workflow manager; responding to the broadcast task messages by a respective processing node; processing the task based on the respective workflow graph in the broadcast task message, and including a result of the processing for each workflow graph in a result message; broadcasting the result messages by the respective processing node; accumulating the broadcast result messages responsive to respective workflow graphs; and outputting the accumulated results to a user or an external system.

Open distributed asynchronous dependency-based content processing

An audio keyword searcher arranged to identify a voice segment of a received audio signal; identify, by an automatic speech recognition engine, one or more phonemes included in the voice segment; output, from the automatic speech recognition engine, the one or more phonemes to a keyword filter to detect whether the voice segment includes any of the one or more first keywords of the first keyword list and, if detected, output the one or more phonemes included in the voice segment to a decoder but, if not detected, not output the one or more phonemes included in the voice segment to the decoder. If the one or more phonemes are output to the decoder: generate a word lattice associated with the voice segment; search the word lattice for one or more second keywords, and determine whether the voice segment includes the one or more second keywords.

Systems and methods for fast filtering of audio keyword search

A voice topic spotting system includes a learning module and a voice topic classifier module. The learning module receives training audio segments with topic labels and generates a fast keyword filter model based on a set of topic-indicative words and generates a topic identification model based on a training set of topic keyword-containing lattices. The voice topic classifier module includes an automatic speech recognition engine arranged to identify one or more keywords included in a received audio segment and output the one or more keywords. A fast keyword filter, implements the fast keyword model to output the received audio segment if a topic-indicative word is detected in the audio segment. A decoder generates a topic keyword-containing lattice associated with the audio segment. A voice topic classifier implements the voice topic identification model to determine a topic associated with received audio segment.

Systems and methods for voice topic spotting

A system for identifying audio data includes a feature extraction module receiving unknown input audio data and dividing the unknown input audio data into a plurality of segments of unknown input audio data. A similarity module receives the plurality of segments of the unknown input audio data and receives known audio data from a known source, the known audio data being divided into a plurality of segments of known audio data. The similarity module performs comparisons between the segments of unknown input audio data and respective segments of known audio data and generates a respective plurality of similarity values representative of similarity between the segments of the comparisons, the comparisons being performed serially. The similarity module terminates the comparisons if the similarity values indicate insufficient similarity between the segments of the comparisons, prior to completing comparisons for all segments of the unknown input audio data.

System and method for speaker identification in audio data

A system for identifying a language in audio data includes a feature extraction module for receiving an unknown input audio data stream and dividing the unknown input audio data stream into segments. A similarity module receives the segments and receives known-language audio data models for known languages. For each segment, the similarity module performs comparisons between the segment and the known-language audio data models and generates probability values representative of the probabilities that the segment includes audio data of the known languages. A processor receives the probability values for each segment and computes an entropy value for the probabilities for each segment. If the entropy value for a segment is less than the entropy value for a previous segment, the processor terminates the comparisons prior to completing comparisons for all segments.

Growing community of inventors

Annapolis, MD, United States of America

Jonathan C Wintrode