Abstract: 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.

Abstract: A method includes obtaining input audio data that captures multiple conversations between speakers and extracting features of segments of the input audio data. The method also includes generating at least a portion of a similarity matrix based on the extracted features, where the similarity matrix identifies similarities of the segments of the input audio data to one another. The method further includes identifying dissimilarity values associated with different corresponding regions of the similarity matrix that are associated with different possible conversation changes. In addition, the method includes identifying one or more locations of conversation changes within the input audio data based on the dissimilarity values.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.