English(EN)BaltiVoice: A Speech Corpus and Fine-tuned Whisper ASR System for the Balti Language
新研究通过新方法应对多语言ASR挑战
作者PulseAugur 编辑部·[9 个来源]·
研究人员正在探索多语言自动语音识别（ASR）的新方法，特别是在单次对话中使用多种语言的语码转换场景。一篇论文研究了通过模型合并将语码转换能力推广到未见过语言对的通用性，但成功有限。另一个项目BaltiVoice为巴尔蒂语引入了一个新的语音语料库和微调的Whisper模型，显著提高了ASR准确性。此外，一个名为WAXAL-NET的系统表明，专门的、较小的ASR模型在非洲语言方面可以优于大型多语言模型，并且一个实时多语言ASR系统使用小型、专业化模型的路由方法来实现高准确性和效率。
AI
arXiv:2606.05846v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) has become a key technology for human--AI interaction. However, code-switching ASR (CS-ASR) remains particularly challenging due to the severe scarcity of multilingual CS speech resources across di…
Automatic Speech Recognition (ASR) has become a key technology for human--AI interaction. However, code-switching ASR (CS-ASR) remains particularly challenging due to the severe scarcity of multilingual CS speech resources across diverse language pairs. Existing approaches primar…
Code-switching automatic speech recognition models show limited generalization across unseen language pairs despite attempts at model merging and domain generalization techniques.
arXiv:2606.03504v1 Announce Type: cross Abstract: We present BaltiVoice, a 16.8-hour read-speech corpus for Balti (ISO 639-3: bft), a Tibetic language spoken in Gilgit-Baltistan, Pakistan, with no prior publicly available ASR resources. The corpus contains 10,060 validated uttera…
We present BaltiVoice, a 16.8-hour read-speech corpus for Balti (ISO 639-3: bft), a Tibetic language spoken in Gilgit-Baltistan, Pakistan, with no prior publicly available ASR resources. The corpus contains 10,060 validated utterances in native Nastaliq script, derived from Mozil…
We present BaltiVoice, a 16.8-hour read-speech corpus for Balti (ISO 639-3: bft), a Tibetic language spoken in Gilgit-Baltistan, Pakistan, with no prior publicly available ASR resources. The corpus contains 10,060 validated utterances in native Nastaliq script, derived from Mozil…
arXiv:2606.02375v1 Announce Type: new Abstract: We evaluate whether compact domain-specialized ASR models can outperform massively multilingual foundation models for conversational African speech across 19 languages in the WAXAL corpus. Fine-tuned edge models achieve a macro-aver…
We evaluate whether compact domain-specialized ASR models can outperform massively multilingual foundation models for conversational African speech across 19 languages in the WAXAL corpus. Fine-tuned edge models achieve a macro-averaged WER of $38.0\%$ compared to $64.9\%$ for th…
<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1ttwfuy/realtime_multilingual_asr_using_rolling_buffers/"> <img alt="Real-time multilingual ASR using rolling buffers and monolingual models [P]" src="https://preview.redd.it/qu5jir6i0p4h1.png?width=140&a…
