Language Proficiency and F0 Entrainment: A Study of L2 English Imitation in Italian, French, and Slovak Speakers

• URL: http://arxiv.org/abs/2404.10440v1
• Date: Tue, 16 Apr 2024 10:10:19 GMT
• Title: Language Proficiency and F0 Entrainment: A Study of L2 English Imitation in Italian, French, and Slovak Speakers
• Authors: Zheng Yuan, Štefan Beňuš, Alessandro D'Ausilio,
• Abstract summary: This study explores F0 entrainment in second language (L2) English speech imitation during an Alternating Reading Task (ART) participants with Italian, French, and Slovak native languages imitated English utterances. Results indicate a nuanced relationship between L2 English proficiency and entrainment.
• Score: 48.3822861675732
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study explores F0 entrainment in second language (L2) English speech imitation during an Alternating Reading Task (ART). Participants with Italian, French, and Slovak native languages imitated English utterances, and their F0 entrainment was quantified using the Dynamic Time Warping (DTW) distance between the parameterized F0 contours of the imitated utterances and those of the model utterances. Results indicate a nuanced relationship between L2 English proficiency and entrainment: speakers with higher proficiency generally exhibit less entrainment in pitch variation and declination. However, within dyads, the more proficient speakers demonstrate a greater ability to mimic pitch range, leading to increased entrainment. This suggests that proficiency influences entrainment differently at individual and dyadic levels, highlighting the complex interplay between language skill and prosodic adaptation.

Related papers

• Probing the Emergence of Cross-lingual Alignment during LLM Training [10.053333786023089]
  Multilingual Large Language Models (LLMs) achieve remarkable levels of zero-shot cross-lingual transfer performance. We study how such cross-lingual alignment emerges during pre-training of LLMs. We observe a high correlation between neuron overlap and downstream performance.
  arXiv  Detail & Related papers  (2024-06-19T05:31:59Z)
• Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly [53.04368883943773]
  Two approaches are proposed to address this, i.e., multilingual pretraining and multilingual instruction tuning. We propose CLiKA to assess the cross-lingual knowledge alignment of LLMs in the Performance, Consistency and Conductivity levels. Results show that while both multilingual pretraining and instruction tuning are beneficial for cross-lingual knowledge alignment, the training strategy needs to be carefully designed.
  arXiv  Detail & Related papers  (2024-04-06T15:25:06Z)
• Could We Have Had Better Multilingual LLMs If English Was Not the Central Language? [4.655168524016426]
  Large Language Models (LLMs) demonstrate strong machine translation capabilities on languages they are trained on. Our study delves into Llama2's translation capabilities. Our experiments show that the 7B Llama2 model yields above 10 BLEU when translating into all languages it has seen.
  arXiv  Detail & Related papers  (2024-02-21T16:32:38Z)
• VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning [56.47303426167584]
  We propose a cross-lingual pre-trained model VECO2.0 based on contrastive learning with multi-granularity alignments. Specifically, the sequence-to-sequence alignment is induced to maximize the similarity of the parallel pairs and minimize the non-parallel pairs. token-to-token alignment is integrated to bridge the gap between synonymous tokens excavated via the thesaurus dictionary from the other unpaired tokens in a bilingual instance.
  arXiv  Detail & Related papers  (2023-04-17T12:23:41Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation [84.59697583372888]
  Neural chat translation (NCT) aims to translate a cross-lingual chat between speakers of different languages. Existing context-aware NMT models cannot achieve satisfactory performances due to limited resources of annotated bilingual dialogues. We propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues.
  arXiv  Detail & Related papers  (2023-01-27T14:41:16Z)
• Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillation [8.718749742587857]
  Cross-lingual summarization models rely on the self-attention mechanism to attend among tokens in two languages. We propose a novel Knowledge-Distillation-based framework for Cross-Lingual Summarization. Our method outperforms state-of-the-art models under both high and low-resourced settings.
  arXiv  Detail & Related papers  (2021-12-07T03:45:02Z)
• A bifurcation threshold for contact-induced language change [0.0]
  This paper proposes a mathematical model of such situations based on reinforcement learning and nonlinear dynamics. The model is evaluated with the help of two case studies, morphological levelling in Afrikaans and the erosion of null subjects in Afro-Peruvian Spanish.
  arXiv  Detail & Related papers  (2021-11-23T18:21:12Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.