Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions
This paper presents an audio indexing system to search for known advertisements in radio broadcast streams, using automatically acquired segmental units. These units, called Automatic Language Independent Speech Processing (ALISP) units, are acquired using temporal decomposition and vector quantization and modeled by Hidden Markov Models (HMMs). To detect commercials, ALISP transcriptions of reference advertisements are compared to the transcriptions of the test radio stream using the Levenshtein distance. The system is described and evaluated on one day broadcast audio streams from 11 French radio stations containing 2070 advertisements. With a set of 2,172 reference advertisements we achieve a mean precision rate of 99% with the corresponding recall value of 96%. Moreover, this system allowed us to detect some annotation errors.
alisp tools advertisement detection copy detection data-driven speech segmentation hmm models