Accuracy
Making a meaningful comparison of the accuracy of different speech analytics systems can be difficult. The output of LVCSR systems can be scored against reference word-level transcriptions to produce a value for the word error rate (WER), but because phonetic systems use phones as the basic recognition unit, rather than words, comparisons using this measure cannot be made.
When speech analytics systems are used to search for spoken words or phrases, what matters to the user is the accuracy of the search results that are returned. Because the impact of individual recognition errors on these search results can vary greatly, measures such as word error rate are not always helpful in determining overall search accuracy from the user perspective.
Measures such as precision and recall, commonly used in the field of information retrieval, are typical ways of quantifying the response of a speech analytics search system. Precision measures the proportion of search results that are relevant to the query. Recall measures the proportion of the total number of relevant items that were returned by the search results. Where a standardised test set has been used, measures such as precision and recall can be used to directly compare the search performance of different speech analytics systems.
These measures of accuracy can be illustrated by the following example. Imagine a user searches a set of audio files for a specific phrase, and the search returns 10 files. If 9 of the 10 search results do in fact contain the search phrase, the precision is 90% (9 out of 10). If the total number of files that actually contain the phrase is 18 then the recall is 50% (9 out of 18).
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