Update README.md

README.md

@@ -18,9 +18,47 @@ metrics:
 
 # Frame classification for filled pauses
 
+## Paper
+```bibtex
+@inproceedings{ljubesic-etal-2025-identifying,
+    title = "Identifying Filled Pauses in Speech Across South and {W}est {S}lavic Languages",
+    author = "Ljube{\v{s}}i{\'c}, Nikola  and
+      Porupski, Ivan  and
+      Rupnik, Peter",
+    editor = "Piskorski, Jakub  and
+      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
+      Nakov, Preslav  and
+      Yangarber, Roman  and
+      Marcinczuk, Michal",
+    booktitle = "Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)",
+    month = jul,
+    year = "2025",
+    address = "Vienna, Austria",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.bsnlp-1.1/",
+    doi = "10.18653/v1/2025.bsnlp-1.1",
+    pages = "1--8",
+    ISBN = "978-1-959429-57-9",
+    abstract = "Filled pauses are among the most common paralinguistic features of speech, yet they are mainly omitted from transcripts. We propose a transformer-based approach for detecting filled pauses directly from the speech signal, fine-tuned on Slovenian and evaluated across South and West Slavic languages. Our results show that speech transformers achieve excellent performance in detecting filled pauses when evaluated in the in-language scenario. We further evaluate cross-lingual capabilities of the model on two closely related South Slavic languages (Croatian and Serbian) and two less closely related West Slavic languages (Czech and Polish). Our results reveal strong cross-lingual generalization capabilities of the model, with only minor performance drops. Moreover, error analysis reveals that the model outperforms human annotators in recall and F1 score, while trailing slightly in precision. In addition to evaluating the capabilities of speech transformers for filled pause detection across Slavic languages, we release new multilingual test datasets and make our fine-tuned model publicly available to support further research and applications in spoken language processing."
+}
+```
+## Model Details
+
 This model classifies individual 20ms frames of audio based on 
 presence of filled pauses ("eee", "errm", ...).
 
+### Model Description
+
+
+
+- **Developed by:** Peter Rupnik, Nikola Ljubešić, Darinka Verdonik, Simona
+  Majhenič
+- **Funded by:** MEZZANINE project
+- **Model type:** Wav2Vec2Bert for Audio Frame Classification
+- **Language(s) (NLP):** Trained and tested on [ROG-Artur](http://hdl.handle.net/11356/1992)
+- **Finetuned from model:** facebook/w2v-bert-2.0
+
+
 
 # Training data
 
@@ -28,6 +66,16 @@ The model was trained on human-annotated Slovenian speech corpus
 [ROG-Artur](http://hdl.handle.net/11356/1992). Recordings from the train split were segmented into 
 at most 30s long chunks. 
 
+## Training Details
+
+| hyperparameter              | value |
+| --------------------------- | ----- |
+| learning rate               | 3e-5  |
+| effective batch size        | 16    |
+| num train epochs            | 20    |
+
+
+
 # Evaluation
 
 Although the output of the model is a series 0 or 1, describing their  20ms frames, 
@@ -182,5 +230,4 @@ print(ds["intervals"][0])
 
 
 
-# Citation
-Coming soon.
+