Updated README

README.md

@@ -28,13 +28,23 @@ The pre-training datasets include: AudioSet (vocalization), FreeSound (babies),
 
 We evaluate voc2vec on six datasets: ASVP-ESD, ASPV-ESD (babies), CNVVE, NonVerbal Vocalization Dataset, Donate a Cry, VIVAE.
 
+The following table reports the average performance in terms of Unweighted Average Recall (UAR) and F1 Macro across the six datasets described above.
+ 
+| Model | Architecture | Pre-training DS | UAR | F1 Macro |
+|--------|-------------|-------------|-----------|-----------|
+| **voc2vec** | wav2vec 2.0 | Voc125 | .612±.212 | .580±.230 |
+| **voc2vec-as-pt** | wav2vec 2.0 | AudioSet + Voc125 | .603±.183 | .574±.194 |
+| **voc2vec-ls-pt** | wav2vec 2.0 | LibriSpeech + Voc125 | .661±.206 | .636±.223 |
+| **voc2vec-hubert-ls-pt** | HuBERT | LibriSpeech + Voc125 | **.696±.189** | **.678±.200** |
+
 ## Available Models
 
 | Model | Description | Link |
 |--------|-------------|------|
 | **voc2vec** | Pre-trained model on **125 hours of non-verbal audio**. | [🔗 Model](https://huggingface.co/alkiskoudounas/voc2vec) |
-| **voc2vec-as-pt** | Continues pre-training from a model that was **initially trained on the AudioSet dataset**. | [🔗 Model](https://huggingface.co/alkiskoudounas/voc2vec-as-pt) |
-| **voc2vec-ls-pt** | Continues pre-training from a model that was **initially trained on the LibriSpeech dataset**. | [🔗 Model](https://huggingface.co/alkiskoudounas/voc2vec-ls-pt) |
+| **voc2vec-as-pt** | Continues pre-training from a wav2vec2-like model that was **initially trained on the AudioSet dataset**. | [🔗 Model](https://huggingface.co/alkiskoudounas/voc2vec-as-pt) |
+| **voc2vec-ls-pt** | Continues pre-training from a wav2vec2-like model that was **initially trained on the LibriSpeech dataset**. | [🔗 Model](https://huggingface.co/alkiskoudounas/voc2vec-ls-pt) |
+| **voc2vec-hubert-ls-pt** | Continues pre-training from a hubert-like model that was **initially trained on the LibriSpeech dataset**. | [🔗 Model](https://huggingface.co/alkiskoudounas/voc2vec-hubert-ls-pt) |
 
 ## Usage examples
 
@@ -63,13 +73,12 @@ logits = model(**inputs).logits
 ```bibtex
 @INPROCEEDINGS{koudounas2025icassp,
   author={Koudounas, Alkis and La Quatra, Moreno and Siniscalchi, Sabato Marco and Baralis, Elena},
-  booktitle={ICASSP 2025 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
+  booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
   title={voc2vec: A Foundation Model for Non-Verbal Vocalization}, 
   year={2025},
   volume={},
   number={},
-  pages={},
-  keywords={},
-  doi={}}
-
+  pages={1-5},
+  keywords={Pediatrics;Accuracy;Foundation models;Benchmark testing;Signal processing;Data models;Acoustics;Speech processing;Nonverbal vocalization;Representation Learning;Self-Supervised Models;Pre-trained Models},
+  doi={10.1109/ICASSP49660.2025.10890672}}
 ```