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README.md
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@@ -69,6 +69,7 @@ The following table describes the available features:
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| attention | Same as the pooled statistics but with the attention weights applied.
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| embedding | The standard ECAPA2 speaker embedding.
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The following table describes the available features:
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| Feature Type| Description | Usage | Labels |
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| Local Feature | Non-uniform effective receptive field in the frequency dimension of each frame-level feature.| Abstract features, probably usefull in tasks less related to speaker characteristics. | lfe1, lfe2, lfe3, lfe4
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| Global Feature | Uniform effective receptive field of each frame-level feature in the frequency dimension.| Generally capture intra-speaker variance better then speaker embeddings. E.g. speaker profiling, emotion recognition. | gfe1, gfe2, gfe3, pool
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| Speaker Embedding | Uniform effective receptive field of each frame-level feature in the frequency dimension.| Best for tasks directly depending on the speaker identity (as opposed to speaker characteristics). E.g. speaker verification, speaker diarization. | embedding
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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| attention | Same as the pooled statistics but with the attention weights applied.
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| embedding | The standard ECAPA2 speaker embedding.
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<!--
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The following table describes the available features:
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| Feature Type| Description | Usage | Labels |
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| Local Feature | Non-uniform effective receptive field in the frequency dimension of each frame-level feature.| Abstract features, probably usefull in tasks less related to speaker characteristics. | lfe1, lfe2, lfe3, lfe4
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| Global Feature | Uniform effective receptive field of each frame-level feature in the frequency dimension.| Generally capture intra-speaker variance better then speaker embeddings. E.g. speaker profiling, emotion recognition. | gfe1, gfe2, gfe3, pool
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| Speaker Embedding | Uniform effective receptive field of each frame-level feature in the frequency dimension.| Best for tasks directly depending on the speaker identity (as opposed to speaker characteristics). E.g. speaker verification, speaker diarization. | embedding
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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