Information Retrieval from Unsegmented Broadcast News Audio

International audienceThis paper describes a system for retrieving relevant portions of broadcast news shows starting with only the audio data. A novel method of automatically detecting and removing commercials is presented and shown to increase the performance of the system while also reducing the computational effort required. A sophisticated large vocabulary speech recogniser which produces high-quality transcriptions of the audio and a window-based retrieval system with post-retrieval merging are also described. Results are presented using the 1999 TREC-8 Spoken Document Retrieval data for the task where no story boundaries are known. Experiments investigating the effectiveness of all aspects of the system are described, and the relative benefits of automatically eliminating commercials, enforcing broadcast structure during retrieval, using relevance feedback, changing retrieval parameters and merging during post-processing are shown. An Average Precision of 46.8%, when duplicates are scored as irrelevant, is shown to be achievable using this system, with the corresponding word error rate of the recogniser being 20.5%

An Automatic audio segmentation system for radio newscast

Current web search engines generally do not enable searches into audio files. Informative metadata would allow searches into audio files, but producing such metadata is a tedious manual task. Tools for automatic production of metadata are therefore needed. This project describes the work done on the development of an automatic audio segmentation system which can be used for this metadata extraction. In this work the radio newscast are divided into segments in which there is only one speaker. Audio features used in this project include Mel Frequency Cepstral Coefficients. This feature was extracted from audio files that were stored in a WAV format, using CLAM. Model-Selection-Based segmentation is used to segment audio signals using this feature

An automatic audio classification system for radio newscast

Current web search engines generally do not enable searches into audio files. Informative metadata would allow searches into audio files, but producing such metadata is a tedious manual task. Tools for automatic production of metadata are therefore needed. This project describes the work done on the development of an automatic audio classification system which can be used for this metadata extraction. In order to design this system I used adapting it to our case of study, the matlab code of the MPEG-7 Experimental Model [15]

Information Retrieval from Unsegmented Broadcast News Audio

International audienceThis paper describes a system for retrieving relevant portions of broadcast news shows starting with only the audio data. A novel method of automatically detecting and removing commercials is presented and shown to increase the performance of the system while also reducing the computational effort required. A sophisticated large vocabulary speech recogniser which produces high-quality transcriptions of the audio and a window-based retrieval system with post-retrieval merging are also described. Results are presented using the 1999 TREC-8 Spoken Document Retrieval data for the task where no story boundaries are known. Experiments investigating the effectiveness of all aspects of the system are described, and the relative benefits of automatically eliminating commercials, enforcing broadcast structure during retrieval, using relevance feedback, changing retrieval parameters and merging during post-processing are shown. An Average Precision of 46.8%, when duplicates are scored as irrelevant, is shown to be achievable using this system, with the corresponding word error rate of the recogniser being 20.5%