News-on-Demand: Types of Errors
Of course, a
fully automated system like News-on-Demand will inevitably make errors.
We distinguish five types of errors, all of which are subjects of active research
aimed at improving the system:
- False segmentation of stories. This happens when we incorrectly
identify the beginning and end of a video paragraph associated with a
single news story. Incorrect segmentation is usually
due to inaccurate transcription, either from true closed-captioning errors,
errors in processing the closed-captioning text into stories, or errors
in the segmentation based on the speech transcription.
- False words in transcripts. Errors in the transcript
are either the result of faulty speech recognition or
errors in the closed-captioned text. The result is the appearance of
incorrect words in stories
and consequent errors in the index and in retrieval.
- False synchronization designates retrieved words that
were actually spoken elsewhere in the video. This is generally
due to closed-captioning that does not match the words returned by
- Incorrectly recognized query. This is the result of an incorrect
speech recognition during the library exploration. The user can
edit and correct query misrecognitions through typing, or simply
repeat or rephrase the query.
- Incorrect set of stories returned for a query. This type of error
is measured through information recall and precision. The user
might get stories that are not relevant to the query or miss relevant
stories. Some of these are caused by previously mentioned problems,
and others are the result of shortcomings in the processing of the
query into retrieval keywords.