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# Spoken Language Identification

The following diagram shows the modules in HPE IDOL Speech Server that enable spoken language identification in a single step.

| The `wav` module reads the audio file and prepares windowed data. a is the audio window series. The `frontend` module takes each window of samples and converts it to a feature vector. f is the feature vector series. The `normalizer` module adjusts the feature vectors to produce normalized feature vectors. nf is the normalized feature vector series. The `lidfeature` module searches the feature vector time series to create phoneme-based language identification features. lf is the language identification feature. The `langid` module analyzes the features and determines the identified languages in the audio data. w is the output time-marked word series. The `lidout` module prepares the output language labels and time positions for storage and result reporting.

The schema that implements this feature is:

    [MyLangId]
    a ← wav (MONO, input)
    f ← frontend (_, a)
    nf ← normalizer (_, f)
    lf ← lidfeature (_, nf)
    lid ← langid (CUMULATIVE, lf)
    output ← lidout (_, lid)

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