arXiv:2609. 11786v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems and audio language models (audio LMs) now report low error rates on monolingual benchmarks, but their behavior on code switched speech in low resource, diacritic rich languages remains poorly characterized.
By Chibuzor Okocha, Christan Earl Grant
arXiv:2606. 19381v1 Announce Type: cross Abstract: Code-switch (CS) Automatic Speech Recognition (ASR) remains challenging due to limited availability of high quality CS text-speech pairs for training.
By Yue Heng Yeo, Haoyang Li, Yizhou Peng, Shreyas Gopal, Hexin Liu, Leibny Paola Garcia-Perera, Hardik B. Sailor, Jeremy H. M. Wong, Eng Siong Chng
VietPrism is a newly released, large‑scale Vietnamese speech corpus that combines 993.4 hours of real utterances from 1,262 verified speakers with 3.1 k hours of synthetic spoof speech. It uniquely offers transcripts, consistent speaker identities, five dialect groups, and extensive Vietnamese‑English code‑switching—nearly half of the corpus—while pairing each spoof with a matched bona fide utterance. The dataset enables controlled evaluation of deep‑fake detection models, revealing significant variability in detector performance across dialects and speaker similarity.
By Minh Hoang, Thai Le
Current speech translation systems, including SpeechLLMs, are trained on cleaned text and tend to strip disfluencies like filled pauses and false starts rather than translate them. We show this comes at a cost: disfluencies carry meaning that gets lost when speech is cleaned up.
arXiv:2609.01016v1 Announce Type: new
Abstract: Current speech synthesis struggles with code-switching, which mixes a foreign language phrase into a primary language utterance, causing the phrase to...
By Che Hyun Lee, Sangkwon Park, Donghun Kang, Dongwook Lee, Youngho Cho, Heeseung Kim, Sungroh Yoon
The paper investigates whether training small decoder-only transformers on code‑switched text can induce cross‑lingual alignment. Using two 100‑million‑word multilingual corpora—one a mix of English, Dutch, and Chinese BabyBabelLM data, and another generated by inserting word‑ and sentence‑level code‑switching via an LLM—the authors find that code‑switched training aligns representations of parallel text, especially across different scripts, and that this alignment persists when later training on monolingual documents. A curriculum that progresses from word‑level code‑switching to sentence‑level code‑switching and finally to monolingual data yields models that outperform baselines on the BabyLM evaluation suite, demonstrating that code‑switching curriculum learning is an effective data augmentation strategy for multilingual pretraining.
By Dries Rooryck, Alex Cai, Yonatan Belinkov, David Alvarez-Melis, Kiant\'e Brantley