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# Improve Performance

Some common situations can reduce the performance of speaker identification.

Training and test data differ in audio quality | Try to use training data for each speaker that closely resembles the recording conditions and quality that you expect in the files that you want to process.
Unbalanced speaker thresholds | If IDOL Speech Server has too many false positives, use a higher `Bias` value when you finalize the speaker score thresholds (in the `SpkIdDevelFinal` task). If the false negative rate is too high, use a lower `Bias` value.
Insufficient training or optimization data | Micro Focus recommends that you use at least five minutes of data for each speaker for training, and more if possible. A similar amount of data is required for each speaker for the development tasks if you are training score thresholds for open-set identification. If you use less data, this can compromise the general performance of the speaker templates.
Insufficient ‘unknown’ data used during optimization | This situation can lead to poor speaker thresholds, which in turn lead to excessive false positives.
Very short audio segments are used in identification | Short audio segments can reduce the accuracy of speaker identification. Use the `MinSpeech` and `MinNonSpeech` parameters to specify the minimum size of audio segments, and the `DiscardShort` parameter to set the minimum segment size before discarding the result.

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