The paper presents a method for inferring a speaker’s first language (L1) from their second‑language English (L2) by using token probability scores from large language models (LLMs). By treating each text as a continuation of a prompt that specifies a candidate L1, the authors identify the L1 with the highest probability score. Experiments with Llama 3.1 8B and 70B on TOEFL9, Write and Improve 2024, and BULATS‑L1 datasets show that token‑probability scoring outperforms direct classification, with the 70B model achieving state‑of‑the‑art results across all datasets.
By Ahmet Yavuz Uluslu, Mark Gales, Kate Knill, Gerold Schneider
The paper introduces a native-reference phone‑class geometry that measures second‑language pronunciation deviation without needing pronunciation labels, read‑aloud prompts, or matched native recordings. By averaging self‑supervised representations for each phone‑class in a native speech corpus and applying singular value decomposition, the authors create a compact coordinate system. Projecting L2 utterances into this space, they find that distances to native coordinates correlate negatively with holistic speaking proficiency and pronunciation quality, indicating the geometry captures relevant acoustic‑phonetic information for spontaneous L2 speech.
By Tina Raissi, Nhan Phan, Chenxiao Wang, Mikko Kurimo
The paper presents a method to automatically identify utterances in child-centered daylong audio recordings that can be reliably transcribed by modern ASR systems, enabling accurate transcription of a substantial portion of the speech. On four English corpora, the approach achieves a median WER of 0% and a mean WER of 16% when transcribing 30% of the total speech, compared to a median WER of 52% when transcribing all speech. Word frequency distributions from the automatic transcripts correlate strongly with manual annotations (r = 0.94 overall, r = 0.99 for frequent words).
By Daniil Kocharov, Azarias Galama, Okko R\"as\"anen
The paper introduces a lightweight approach to detect code‑switching (CS) directly from speech by leveraging language‑selective hidden‑state dimensions in pretrained speech encoders. Using monolingual recordings to identify these dimensions, the method aggregates frame‑level activations to predict CS presence, achieving a mean F1 score of 0.798 on the CS‑FLEURS dataset with the Whisper‑small encoder. This demonstrates that pretrained speech models inherently encode CS information, offering a practical tool for expanding multilingual corpora.
By Jie Chi, Zakaria Aldeneh, Masha Fedzechkina, Maureen de Seyssel, Natalie Schluter
arXiv:2608. 09930v1 Announce Type: cross Abstract: Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Models (Audio-LLM) judges) are expected to reflect human perception, yet it is unclear how well they capture the distinct aspects of speech that listeners actually perceive.
By Oluwanifemi Bamgbose, Simon Rosen, Jash Shah, Lindsay Devon Brin, Hoang H Nguyen, Anke Koelzer, Rachel Hansen, Tara Bogavelli, Fanny Riols
SpeechCritic learns a diagnostic speech judge from only about 300 human-labeled comparisons, calibrating a frontier audio-language model with non-binding hints that select acoustic measurements agreeing with human judgments. The approach improves dimension-level agreement by 6.3 points and reduces mismatch in Tie rates by 10.4 points, and training a 7B judge with SFT, OPD, and RL yields distinct behaviors, with RL producing more specific rationales. The method works across language pairs, demonstrating a path from limited human preferences to a diagnostic speech judge.
By Mingyue Huo, Shivam Mehta, Bhavin Jawade, Yinghong Lan, Haoqi Li
The paper introduces methods to improve speech recognition for multilingual video transcription using Whisper-based tools, targeting cross‑cultural understanding. It reports an average transcription error rate of 30% across seven languages, which can be lowered to 20% with modest fine‑tuning. The authors also release associated speech and metadata to aid community refinement of these techniques.
By Michael Picheny
Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding releva...
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
arXiv:2610.26406v2 Announce Type: replace
Abstract: oollim is a locally executable system for evaluating L2 English pronunciation accuracy, fluency, and prosody on a consumer computer. The system com...
By Richard Rose (Hankuk University of Foreign Studies)
OpenPA is a new automatic pronunciation assessment framework that eliminates the need for forced alignment by using Whisper’s cross‑attention to predict word‑ and utterance‑level scores. It adapts to mixed transcripts through mixed‑transcript adaptation and adaptive substitution weighting, enabling a single model to handle both closed‑ and open‑response settings. Trained only on speechocean762, OpenPA outperforms other systems on in‑domain closed and simulated open‑response tests, retains strong zero‑shot performance on MultiPA, and shows promise in oral‑cancer speech evaluation.
By Fu-An Chao, Bi-Cheng Yan, Berlin Chen
The study investigates how automatic speech recognition (ASR) transcript normalization—specifically the removal of filled pauses, punctuation, and colloquial expansions—affects dialogue act classification. By testing nine transcript conditions across six classifiers and three conversational corpora, the authors find that heavy normalization consistently lowers Macro‑F1 scores, especially when using a comprehensive filler lexicon. Punctuation removal and selective filler removal also degrade performance, indicating that the impact of normalization varies with preprocessing choices, feature extraction methods, and the conversational context.
By Lucky Onyekwelu-Udoka, Md Shafiqul Islam