relationship-between-auditory-and-semantic-entrainment
Relationship between auditory and semantic entrainmnet
https://github.com/jaykejriwal/relationship-between-auditory-and-semantic-entrainment
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in README -
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (6.0%) to scientific vocabulary
Keywords
Repository
Relationship between auditory and semantic entrainmnet
Basic Info
- Host: GitHub
- Owner: jaykejriwal
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://www.isca-archive.org/interspeech_2023/kejriwal23b_interspeech.html
- Size: 15.9 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Relationship between auditory and semantic entrainment
Python program for understanding the relationship between auditory and semantic entrainment.
Dataset
We utilized state-of-the-art DNN embeddings such as BERT and TRIpLet Loss network (TRILL) vectors to extract features for measuring semantic and auditory similarities of turns within dialogues in two comparable spoken corpora of two different languages, namely Columbia Games corpus and Slovak Games corpus.
The input folder shows the sample files required for processing.
Columbia Games corpus can be downloaded from https://catalog.ldc.upenn.edu/LDC2021S02
Slovak Games corpus is available upon request by mailing it to the authors
Required Software
ffmpeg (Download from https://www.ffmpeg.org/download.html)
sentence-transformers (pip install sentence-transformers)
tensorflow (pip install tensorflow)
textgrid (Install textgrid from https://github.com/kylebgorman/textgrid)
TRILL vectors model (Download from https://tfhub.dev/google/nonsemantic-speech-benchmark/trill/3)
Execution instruction
Two Jupyter Notebook files are uploaded. Each file presents a step-by-step procedure for extracting features and measuring entrainment in different languages.
Citation
Kejriwal, J., Beňuš, Š. (2023) Relationship between auditory and semantic entrainment using Deep Neural Networks (DNN). Proc. INTERSPEECH 2023, 2623-2627, doi: 10.21437/Interspeech.2023-1947
Owner
- Name: Jay Kejriwal
- Login: jaykejriwal
- Kind: user
- Repositories: 1
- Profile: https://github.com/jaykejriwal
Citation (CITATION.bib)
@inproceedings{kejriwal23b_interspeech,
author={Jay Kejriwal and Štefan Beňuš},
title={{Relationship between auditory and semantic entrainment using Deep Neural Networks (DNN)}},
year=2023,
booktitle={Proc. INTERSPEECH 2023},
pages={2623--2627},
doi={10.21437/Interspeech.2023-1947},
issn={2958-1796}
}