Expired65% confidenceOpinionExact time
The training data of current mainstream large models contains a relatively high proportion of English corpus, which may cause the model to favor English prosodic patterns in syllable boundary processing and pause rhythm, producing a foreign accent
3
Sources
65%
Confidence
Medium-term (~90 days)
Relevance
7/12/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/10/2026(expired)
Sources
Related Claims
UnverifiedEnglish content in mainstream large language model training corpora typically exceeds 50%, leading to significant performance gaps for non-English languages.76% similarUnverified主流扩散模型的文字生成依赖CLIP等视觉-语言预训练模型，其训练数据以英文为主，导致中文字符生成困难74% similarUnverifiedThe training data of most speech models is primarily monolingual, and mixed-language corpora are relatively scarce.74% similarUnverifiedEnglish data accounts for an overwhelming proportion of mainstream large language model training corpora; taking public datasets such as Common Crawl as an example, English content typically constitutes 40%–60% of the total, while languages such as Vietnamese and Chinese often account for less than 5%.73% similarUnverifiedThe core challenge of cross-lingual voice cloning lies in the vast differences between the phoneme systems of different languages; modern solutions typically employ language-agnostic intermediate representations (such as IPA phoneme sequences or self-supervised speech representations).73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/489520API
curl https://kongchang.com/api/v1/knowledge/claims/489520MCP
get_claim(id=489520)