Update README.md

README.md

@@ -62,6 +62,15 @@ feature = ecapa2_model(audio, label='embedding|gfe_1|pool')
 
 The following table describes the available features:
 
+| Feature ID| Description | Usage | Labels |
+| ----------- | ----------- | ----------- | ----------- |
+| gfe_1, gfe_2 | Mean and variance of frame-level features as indicated in Figure 1, extracted before ReLU and BatchNorm layer.| Furthest from speaker embedding, probably usefull in tasks less related to speaker characteristics.
+| pool | Pooled statistics (mean and variance) before the bottleneck speaker embedding layer, extracted before ReLU layer.| Generally capture intra-speaker variance better then speaker embeddings. E.g. speaker profiling, emotion recognition.
+| attention | Same as the pooled statistics but with the attention weights applied.| Generally capture intra-speaker variance better then speaker embeddings. E.g. speaker profiling, emotion recognition.
+| embedding | The standard ECAPA2 speaker embedding. | Best for tasks directly depending on the speaker identity (as opposed to speaker characteristics). E.g. speaker verification, speaker diarization.
+
+The following table describes the available features:
+
 | Feature Type| Description | Usage | Labels |
 | ----------- | ----------- | ----------- | ----------- |
 | 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