ProjectPhylomilia – Phylogenetic linguistic inference from acoustic speech data
Basic data
Acronym:
Phylomilia
Title:
Phylogenetic linguistic inference from acoustic speech data
Duration:
01/12/2024 to 30/11/2027
Abstract / short description:
Computational comparative linguistics traditionally relies heavily on manual data preprocessing, which limits progress, scalability, and reproducibility. This project aims to revolutionize this field by leveraging advanced Deep Learning methodologies, specifically focusing on automatic speech processing, to perform phylogenetic analysis directly from acoustic speech data without manual intervention. Utilizing speech as the primary data source marks a significant shift from writing-based analyses, allowing for more direct and nuanced insights into language evolution.
The proposed methodology simplifies the traditional multi-step workflow of linguistic analysis into two core processes:
1. Transforming speech data into vector space representations using self-supervised Deep Learning models such as wav2vec-u, which effectively captures the linguistic features directly from audio data.
2. Conducting phylogenetic inference from these vectorized representations to construct
language family trees and deduce historical language relationships.
As preparation for the pre-training for the first step, the project will device a language-
independent end-to-end automatic speech recognition tool that transcribes spoken language into IPA.
Leveraging autoencoder techniques, the project will, furthermore, probabilistically reconstruct aspects of the vocabulary and phonotactics of earlier language stages.
As the Deep-Learning methods utilized in the project have a black-box character, the project will, finally, devote attention to post-hoc explainability of the trained model in linguistic terms.
The proposed methodology simplifies the traditional multi-step workflow of linguistic analysis into two core processes:
1. Transforming speech data into vector space representations using self-supervised Deep Learning models such as wav2vec-u, which effectively captures the linguistic features directly from audio data.
2. Conducting phylogenetic inference from these vectorized representations to construct
language family trees and deduce historical language relationships.
As preparation for the pre-training for the first step, the project will device a language-
independent end-to-end automatic speech recognition tool that transcribes spoken language into IPA.
Leveraging autoencoder techniques, the project will, furthermore, probabilistically reconstruct aspects of the vocabulary and phonotactics of earlier language stages.
As the Deep-Learning methods utilized in the project have a black-box character, the project will, finally, devote attention to post-hoc explainability of the trained model in linguistic terms.
Keywords:
deep learning
automatic speech recognition
phylogenetics
linguistics
Linguistik
Involved staff
Managers
Institute of Linguistics (SfS)
Department of Modern Languages, Faculty of Humanities
Department of Modern Languages, Faculty of Humanities
CRC 833 - Construction of Meaning: The Dynamics and Adaptivity of Linguistic Structures
Collaborative research centers and transregios
Collaborative research centers and transregios
Contact persons
Institute of Linguistics (SfS)
Department of Modern Languages, Faculty of Humanities
Department of Modern Languages, Faculty of Humanities
CRC 833 - Construction of Meaning: The Dynamics and Adaptivity of Linguistic Structures
Collaborative research centers and transregios
Collaborative research centers and transregios
Local organizational units
Institute of Linguistics (SfS)
Department of Modern Languages
Faculty of Humanities
Faculty of Humanities
Funders
Hannover, Niedersachsen, Germany