Abstract: Speakers are automatically identified in an audio (or video) source. The audio information is processed to identify potential segment boundaries. Homogeneous segments are clustered substantially concurrently with the segmentation routine, and a cluster identifier is assigned to each identified segment. A segmentation subroutine identifies potential segment boundaries using the BIC model selection criterion. A clustering subroutine uses a BIC model selection criterion to assign a cluster identifier to each of the identified segments. If the difference of BIC values for each model is positive, the two clusters are merged.

Abstract: A method and apparatus are disclosed for identifying speakers participating in an audio-video source, whether or not such speakers have been previously registered or enrolled. The speaker identification system uses an enrolled speaker database that includes background models for unenrolled speakers, such as “unenrolled male” or “unenrolled female,” to assign a speaker label to each identified segment. Speaker labels are identified for each speech segment by comparing the segment utterances to the enrolled speaker database and finding the “closest” speaker, if any. A speech segment having an unknown speaker is initially assigned a general speaker label from the set of background models. The “unenrolled” segment is assigned a segment number and receives a cluster identifier assigned by the clustering system.

Abstract: A method and apparatus are disclosed for automatically transcribing audio information from an audio-video source and concurrently identifying the speakers. The disclosed audio transcription and speaker classification system includes a speech recognition system, a speaker segmentation system and a speaker identification system. A common front-end processor computes feature vectors that are processed along parallel branches in a multi-threaded environment by the speech recognition system, speaker segmentation system and speaker identification system, for example, using a shared memory architecture that acts in a server-like manner to distribute the computed feature vectors to a channel associated with each parallel branch. The speech recognition system produces transcripts with time-alignments for each word in the transcript. The speaker segmentation system separates the speakers and identifies all possible frames where there is a segment boundary between non-homogeneous speech portions.

Abstract: Methods and apparatus are provided for retrieving audio information based on the audio content as well as the identity of the speaker. The results of content and speaker-based audio information retrieval methods are combined to provide references to audio information (and indirectly to video). A query search system retrieves information responsive to a textual query containing a text string (one or more key words), and the identity of a given speaker. An indexing system transcribes and indexes the audio information to create time-stamped content index file(s) and speaker index file(s). An audio retrieval system uses the generated content and speaker indexes to perform query-document matching based on the audio content and the speaker identity. Documents satisfying the user-specified content and speaker constraints are identified by comparing the start and end times of the document segments in both the content and speaker domains.