What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training

• URL: http://arxiv.org/abs/2506.00981v2
• Date: Thu, 10 Jul 2025 12:20:48 GMT
• Title: What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training
• Authors: Marianne de Heer Kloots, Hosein Mohebbi, Charlotte Pouw, Gaofei Shen, Willem Zuidema, Martijn Bentum,
• Abstract summary: Existing work has shown that a range of linguistic features can be successfully decoded from end-to-end models trained only on speech recordings.<n>Here we test the encoding of Dutch phonetic and lexical information in internal representations of self-supervised Wav2Vec2 models.
• Score: 2.8038082486377114
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How language-specific are speech representations learned by self-supervised models? Existing work has shown that a range of linguistic features can be successfully decoded from end-to-end models trained only on speech recordings. However, it's less clear to what extent pre-training on specific languages improves language-specific linguistic information. Here we test the encoding of Dutch phonetic and lexical information in internal representations of self-supervised Wav2Vec2 models. Pre-training exclusively on Dutch improves the representation of Dutch linguistic features as compared to pre-training on similar amounts of English or larger amounts of multilingual data. This language-specific advantage is well-detected by trained clustering or classification probes, and partially observable using zero-shot metrics. Furthermore, the language-specific benefit on linguistic feature encoding aligns with downstream performance on Automatic Speech Recognition.

Related papers

• Cross-Lingual Transfer Learning for Speech Translation [7.802021866251242]
  This paper examines how to expand the speech translation capability of speech foundation models with restricted data.<n>Whisper, a speech foundation model with strong performance on speech recognition and English translation, is used as the example model.<n>Using speech-to-speech retrieval to analyse the audio representations generated by the encoder, we show that utterances from different languages are mapped to a shared semantic space.
  arXiv  Detail & Related papers  (2024-07-01T09:51:48Z)
• Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching [65.74653592668743]
  Finetuning self-supervised multilingual representations reduces absolute word error rates by up to 20%. In circumstances with limited training data finetuning self-supervised representations is a better performing and viable solution.
  arXiv  Detail & Related papers  (2023-11-25T17:05:21Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Learning Cross-lingual Visual Speech Representations [108.68531445641769]
  Cross-lingual self-supervised visual representation learning has been a growing research topic in the last few years. We use the recently-proposed Raw Audio-Visual Speechs (RAVEn) framework to pre-train an audio-visual model with unlabelled data. Our experiments show that: (1) multi-lingual models with more data outperform monolingual ones, but, when keeping the amount of data fixed, monolingual models tend to reach better performance.
  arXiv  Detail & Related papers  (2023-03-14T17:05:08Z)
• Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining [2.3513645401551333]
  We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language. We find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance.
  arXiv  Detail & Related papers  (2023-01-18T03:57:53Z)
• M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrieval [56.49878599920353]
  This work investigates the use of large-scale, English-only pre-trained models (CLIP and HuBERT) for multilingual image-speech retrieval. For non-English image-speech retrieval, we outperform the current state-of-the-art performance by a wide margin both when training separate models for each language, and with a single model which processes speech in all three languages.
  arXiv  Detail & Related papers  (2022-11-02T14:54:45Z)
• Simple and Effective Zero-shot Cross-lingual Phoneme Recognition [46.76787843369816]
  This paper extends previous work on zero-shot cross-lingual transfer learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. Experiments show that this simple method significantly outperforms prior work which introduced task-specific architectures.
  arXiv  Detail & Related papers  (2021-09-23T22:50:32Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision [110.66085917826648]
  We develop a technique that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images. "vokenization" is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks.
  arXiv  Detail & Related papers  (2020-10-14T02:11:51Z)
• Learning Spoken Language Representations with Neural Lattice Language Modeling [39.50831917042577]
  We propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks. The proposed two-stage pre-training approach reduces the demands of speech data and has better efficiency.
  arXiv  Detail & Related papers  (2020-07-06T10:38:03Z)
• Multilingual Jointly Trained Acoustic and Written Word Embeddings [22.63696520064212]
  We extend this idea to multiple low-resource languages. We jointly train an AWE model and an AGWE model, using phonetically transcribed data from multiple languages. The pre-trained models can then be used for unseen zero-resource languages, or fine-tuned on data from low-resource languages.
  arXiv  Detail & Related papers  (2020-06-24T19:16:02Z)
• CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning [11.552745999302905]
  More than half of the 7,000 languages in the world are in imminent danger of going extinct. It is relatively easy to obtain textual translations corresponding to speech. We construct a convolutional neural network audio encoder capable of extracting linguistic representations from speech.
  arXiv  Detail & Related papers  (2020-06-04T12:21:48Z)

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.