A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora
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Abstract (arXiv)
We propose a method of simulating the human process of foreign accentuation using Generative Spoken Language Model (GSLM) only with native speech corpora. When one listens to spoken words of a foreign language and repeats them, the repeated speech is often with the accent of that listener's L1. This is said to be because the spoken words are mentally represented as a sequence of phonological units of the L1, and those units are used for oral reproduction. We simulate this process by inputting speech of language A into GSLM of language B to add B's accent onto the input speech. The process of running ASR of the L1 for foreign input speech and giving the ASR result to TTS of the L1 can be viewed as a naive implementation of this approach. The results of our experiments show that the synthesized accent of the output speech is highly natural, compared to real samples of A generated by speakers whose L1 is B, and that the degree of accentuation is controllable.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 16 Jul 2024.
Builds on
9 of 37 references.
References · 9
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
- 2021Generative Spoken Language Modeling from Raw Audiocited by 340
- 2020Universal Phone Recognition with a Multilingual Allophone Systemcited by 81
- 2017JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesiscited by 58
- 2019CSS10: A Collection of Single Speaker Speech Datasets for 10 Languagescited by 54
- 2019JVS corpus: free Japanese multi-speaker voice corpuscited by 22
- 2022J-MAC: Japanese multi-speaker audiobook corpus for speech synthesiscited by 4
- 2022Controllable Accented Text-to-Speech Synthesiscited by 2
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Citing papers per year: 2026: 2.
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