Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of Proto-Indo-European

Hartmann, Frederik

doi:10.18653/v1/w19-4713

Cited by 7 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the probability of this being a random outlier is relatively low given that the clade is strongly supported by the data, this clustering might be an indication that *m phonotactically differs from other sonorants. An inconsistency within the pie nasal series was already detected in Hartmann (2019). Although this is an accidental finding, it ties in with recent discussions on the sonority of pie */m/ in the context of the pie sonority hierarchy.8…”

Section: Local Predictability and Statistical Constraint Effects In Piesupporting

confidence: 62%

The phonetic value of the Proto-Indo-European laryngeals

Hartmann

2021

Indo-Eur. Linguist.

Self Cite

View full text Add to dashboard Cite

Discussion of the exact phonetic value of the so-called ‘laryngeals’ in Proto-Indo-European has been ongoing ever since their discovery, and no uniform consensus has yet been reached. This paper aims at introducing a new method to determine the quality of the laryngeals that differs substantially from traditional techniques previously applied to this problem, by making use of deep neural networks as part of the larger field of machine learning algorithms. Phonetic environment data serves as the basis for training the networks, enabling the algorithm to determine sound features solely by their immediate phonetic neighbors. It proves possible to assess the phonetic features of the laryngeals computationally and to propose a quantitatively founded interpretation.

show abstract

Section: Local Predictability and Statistical Constraint Effects In Piesupporting

confidence: 62%

The phonetic value of the Proto-Indo-European laryngeals

Hartmann

2021

Indo-Eur. Linguist.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We are also not the first researchers to explore neural modeling of phonetic features. Hartmann (2019Hartmann ( , 2021 showed that neural networks can predict features of Proto-Indo-European phones given the features of a trigram context, which reflect SYN-CHRONIC (applying at a particular stage in a language's history) phonetic phenomena. Our neural network, on the other hand, predicts the probability of feature changes in a sound change, given DI-ACHRONIC data (data that represents change over time).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

2023

View full text Add to dashboard Cite

Welcome to the 4th International Workshop on Computational Approaches to Historical Language Change (LChange'23) co-located with EMNLP 2023. LChange is held on December 6th, 2023, as a hybrid event with participation possible both virtually and on-site in Singapore.Characterizing the time-varying nature of language will have broad implications and applications in multiple fields including linguistics, artificial intelligence, digital humanities, computational cognitive and social sciences. In this workshop, we bring together the world's pioneers and experts in computational approaches to historical language change with a focus on digital text corpora. In doing so, this workshop carries out the triple goals of disseminating state-of-the-art research on diachronic modeling of language change, fostering cross-disciplinary collaborations, and exploring the fundamental theoretical and methodological challenges in this growing niche of computational linguistic research.In response to the call, we received 28 submissions. Each of them was carefully evaluated by at least two members of the Program Committee, whom we believed to be most appropriate for each paper. Based on the reviewers' feedback we accepted 17 full and short papers as oral or poster presentations. We had two distinguished keynote presentations: the first by Gemma Boleda (Research Professor in the Department of Translation and Language Sciences of the Universitat Pompeu Fabra, Spain) who presented a talk entitled "What does semantic change have to do with Hello Kitty? Referring as the source of change", and the second by Mario Giulianelli (a postdoctoral fellow at ETH Zurich) with the talk "Neural language models for word usage representation and analysis". Finally, we invited five EMNLP'23 Findings papers to be presented as posters, which are not included in the workshop proceedings.To further support the community, we offered five student scholarships to cover registration fees. We also offered mentoring for four young researchers on their research topic in the field of language change, either during the workshop or virtually.We hope that you will find the workshop papers insightful and inspiring. We would like to thank the keynote speakers for their stimulating talks, the authors of all papers for their interesting contributions, and the members of the Program Committee for their insightful reviews. Our special thanks go to the emergency reviewers who stepped in to provide their expertise. We also express our gratitude to the EMNLP 2023 workshop chairs for their kind assistance during the organization process. Finally, our thanks go to our gold sponsor iguanodon.ai, as well as the research project "Towards Computational Lexical Semantic Change Detection" (Swedish Research Council, contract 2018-01184) and the research program "Change is Key!" (Riksbankens Jubileumsfond, contract M21-0021).

show abstract

“…Phoneme classification for Bengali Language using DPFs and deep neural network was reported in [21]. In [22], a deep neural network is used to predict historical phonetic features drawn upon synchronic phonetic patterns arising from coarticulation and statistical constraints in Proto-Indo-European language. In [23], extracted acoustic features of speech signal using hamming window and pre-emphasis filter, in addition to extracted decompositional features using daubechies-filtered 5th-depth Wavelet Packet Decomposition (WPT), are optimized using genetic algorithm to classify Turkish vowels.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

et al. 2020

View full text Add to dashboard Cite

Distinctive phonetic features have an important role in Arabic speech phoneme recognition. In a given language, distinctive phonetic features are extrapolated from acoustic features using different methods. However, exploiting lengthy acoustic features vector in the sake of phoneme recognition has a huge cost in terms of computational complexity, which in turn, affects real time applications. The aim of this work is to consider methods to reduce the size of features vector employed for distinctive phonetic feature and phoneme recognition. The objective is to select the relevant input features that contribute to the speech recognition process. This, in turn, will lead to a reduced computational complexity of recognition algorithm, and an improved recognition accuracy. In the proposed approach, genetic algorithm is used to perform optimal features selection. Therefore, a baseline model based on feedforward neural networks is first built. This model is used to benchmark the results of proposed features selection method with a method that employs all elements of a features vector. Experimental results, utilizing the King Abdulaziz City for Science and Technology Arabic Phonetic Database, show that the average genetic algorithm based phoneme overall recognition accuracy is maintained slightly higher than that of recognition method employing the full-fledge features vector. The genetic algorithm based distinctive phonetic features recognition method has achieved a 50% reduction in the dimension of the input vector while obtaining a recognition accuracy of 90%. Moreover, the results of the proposed method is validated using Wilcoxon signed rank test.

show abstract

Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of Proto-Indo-European

Cited by 7 publications

References 18 publications

The phonetic value of the Proto-Indo-European laryngeals

The phonetic value of the Proto-Indo-European laryngeals

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

Contact Info

Product

Resources

About