mlsanityData to 14 Oct 2026

Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis

arXiv 2501.06810 ↗First posted 12 Jan 2025eess.AS cs.CL cs.SDcited by 5

Not on the map. Why?

Abstract (arXiv)

This paper examines how linguistic similarity affects cross-lingual phonetic representation in speech processing for low-resource languages, emphasizing effective source language selection. Previous cross-lingual research has used various source languages to enhance performance for the target low-resource language without thorough consideration of selection. Our study stands out by providing an in-depth analysis of language selection, supported by a practical approach to assess phonetic proximity among multiple language families. We investigate how within-family similarity impacts performance in multilingual training, which aids in understanding language dynamics. We also evaluate the effect of using phonologically similar languages, regardless of family. For the phoneme recognition task, utilizing phonologically similar languages consistently achieves a relative improvement of 55.6% over monolingual training, even surpassing the performance of a large-scale self-supervised learning model. Multilingual training within the same language family demonstrates that higher phonological similarity enhances performance, while lower similarity results in degraded performance compared to monolingual training.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 45 references that are in the map. First posted on arXiv 12 Jan 2025.

Builds on

18 of 45 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  4. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  5. 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 501
  6. 2019Choosing Transfer Languages for Cross-Lingual Learningcited by 144
  7. 2018ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentationcited by 133
  8. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  9. 2023Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Reviewcited by 50
  10. 2021Exploiting Adapters for Cross-lingual Low-resource Speech Recognitioncited by 35
  11. 2018Sequence-based Multi-lingual Low Resource Speech Recognitioncited by 20
  12. 2023Universal Automatic Phonetic Transcription into the International Phonetic Alphabetcited by 16
6 more
  1. 2020Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translationcited by 14
  2. 2023Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitchingcited by 11
  3. 2024Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokenscited by 11
  4. 2021Differentiable Allophone Graphs for Language-Universal Speech Recognitioncited by 10
  5. 2019Phoneme Level Language Models for Sequence Based Low Resource ASRcited by 3
  6. 2021The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Inputcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026Multilingual Phonological Feature Recognition with Self-Supervised Speech Modelscited by 2
  2. 2025How Far Do SSL Speech Models Listen for Tone? Temporal Focus of Tone Representation under Low-resource Transfercited by 1
  3. 2026Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Clustercited by 1
  4. 2026Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detectioncited by 1
  5. 2026Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASRcited by 0