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@@ -7,8 +7,8 @@ The 10 speech representations benchmarked in the accompanying paper. Each is loa
 | x-vector | Supervised classification | VoxCeleb 1+2 | 512 | `speechbrain/spkrec-xvect-voxceleb` | `extract_xvector.py` |
 | ECAPA-TDNN | AAM-Softmax | VoxCeleb 1+2 | 192 | `speechbrain/spkrec-ecapa-voxceleb` | `extract_ecapa_tdnn.py` |
 | RawNet3 | AAM-Softmax | VoxCeleb 1+2 | 192 | `espnet/voxcelebs12_rawnet3` | `extract_rawnet3_embeddings.py` |
-| TitaNet (large) | AAM-Softmax | VoxCeleb + Fisher + SWB | 192 | `nvidia/speakerverification_en_titanet_large` | `extract_titanet.py` |
-| resemblyzer | GE2E loss, 3-layer LSTM | LibriSpeech / VoxCeleb (mixed) | 256 | bundled with `resemblyzer` package | `extract_resemblyzer.py` |
+| TitaNet (large) | AAM-Softmax | VoxCeleb 1+2, Fisher, SWB, LibriSpeech, NIST SRE | 192 | `nvidia/speakerverification_en_titanet_large` | `extract_titanet.py` |
+| resemblyzer | GE2E loss, 3-layer LSTM | LibriSpeech + VoxCeleb 1+2 | 256 | bundled with `resemblyzer` package | `extract_resemblyzer.py` |
 | wav2vec 2.0 | Contrastive masked prediction | LibriSpeech 960 h | 768 | `facebook/wav2vec2-base` | `extract_wav2vec2.py` |
 | HuBERT | Masked prediction | LibriSpeech 960 h | 768 | `facebook/hubert-base-ls960` | `extract_hubert.py` |
 | WavLM | Masked prediction + denoising | 94K h mixed | 768 | `microsoft/wavlm-base-plus` | `extract_wavlm.py` |