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# Speaker Identification

IDOL Speech Server provides a set of speaker identification tasks that cover both the training of a set of speaker templates, and the identification of speakers by using this set. These sections cover the speaker identification training process in more detail, and give some basic guidance on how best to optimize the system and run speaker identification tasks. For more detailed information on optimization and speaker identification, and on the default speaker identification tasks, refer to the Speech Server Administration Guide.

## Speaker Identification System Workflow

The process of training and using a speaker identification system involves four main steps:

1. Create templates. You train each speaker template (ATF) from one or more audio files (or selected sections of files) that contain only speech from the specific speaker. This step is performed by using a task such as `SpkIdTrain`, `SpkIdTrainWav`, or `SpkIdTrainStream`, depending on the form of the input data.
2. Score statistics. A range of typical scores for each speaker (for both true and false examples) is then calculated for each speaker template based on a separate development data set. Speech Server stores the analysis of these scores in an ATD file. This step is performed by using a task such as `SpkIdDevel`, `SpkIdDevelWav`, or `SpkIdDevelStream`, depending on the form of the input data.

3. Calculate thresholds. Speech Server uses the score information in the ATD files to estimate a score threshold for each speaker template, and stores the value in the template file. This value is the score below which a result is not considered to be a genuine hit, leading to “Unknown” results in an open-set identification system. This step is performed by using the `SpkIdDevelFinal` task.

4. Identify speakers. Finally, you can use the `SpkIdEvalWav` or `SpkIdEvalStream` tasks to identify known speakers that might be present in your test audio data based on the set of speaker templates.

Note: For a closed-set speaker identification system, it is not necessary to run Step 2 and Step 3, and score thresholds are not required.

## Closed-Set Identification and Open-Set Identification

Open-set speaker identification identifies sections of speech that do not match any of the known speakers as being from an unknown speaker. To achieve this, a score threshold is estimated for each speaker. A hit is considered genuine only if the score is over the threshold estimated for the specific speaker template. After you train the speaker templates, the thresholds are calculated based on typical true and false hit scores for each template generated from a further set of audio samples.

The following example shows an unknown speaker result in the results:

    1     A     10.630     9.460     Unknown_     FEMALE     0.000
    1     A     20.090     6.150     Brown        MALE       6.983

In this segment, from `10.630` to `20.090` none of the trained speakers scored above the relevant threshold, and thus a result of `Unknown_` was returned.

Closed-set speaker identification assumes that all the speakers in the audio data being processed are from the closed speaker set. There are no score thresholds applied in this case. When you run a closed-set identification task, set the `ClosedSet` parameter in the `AudioTemplateScore` module to `True` (you can do this by using tthe `ClosedSet` action parameter for the `SpkIdEvalWav` and `SpkIdEvalStream` speaker identification tasks).

The following example shows the same results as previously, but this time for closed-set identification rather than open-set:

    1     A     10.630     9.460     Smith     FEMALE     0.15200
    1     A     20.090	6.150	  Brown	    MALE       0.863

In this segment, from `10.630` to `20.090` the speaker `Smith` was the top-scoring template and was given as the result (even though the score is relatively low). Note also that the score for `Brown` is different from that shown in the open-set example. When you set `ClosedSet` to `True`, a different score normalization strategy is used that is more suited to closed-set operation, and thus the scores might differ.

Note: An `Unknown_` result might still be given in a closed-set system, but only if the audio segment is determined to be non-speech, or is too short for accurate classification.

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