The paper investigates whether large models are necessary for speech brain‑computer interfaces (BCIs) and introduces BEST, a two‑stage framework that uses hierarchical intermediate representations and a compact decoder to map neural activity directly to text. BEST achieves low word error rates (6.19% and 1.62%) on the Brain‑to‑Text ’24 and ’25 benchmarks while using only 3.85% of the parameters of comparable methods. The study also highlights that decoder scaling yields non‑monotonic gains, cross‑modal alignment is more critical than model size, and overfitting harms sequence‑level performance.
By Zehui Feng, Weichuan Wang, Xiaohan Chen, Cuntai Guan, Ting Han
arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.
By Yuanhao Chen, Peter Chin
PTC-Bias is a two-stage framework that improves contextual biasing in speech large language models by using phoneme-level temporal competition. In the first stage, PTC Retrieval performs frame-synchronous phoneme decoding to generate a compact shortlist of bias words and their speech intervals. The second stage, PTC Correction, applies a local competition between retrieved candidates and mismatched transcript spans within those intervals, reducing near-homophone and word-segmentation errors without extra SpeechLLM passes. Experiments on LibriSpeech demonstrate consistent gains across two SpeechLLMs, with PTC-Bias reducing B-WER by up to 23.9% relative to CTC-Filter while keeping U-WER nearly unchanged.
By Zhiqi Ai, Han Cheng, Shiyi Mu, Yongjin Zhou, Shugong Xu
arXiv:2603. 29042v2 Announce Type: replace-cross Abstract: Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive.
By Shikhar Bharadwaj, Chin-Jou Li, Kwanghee Choi, Eunjung Yeo, William Chen, Shinji Watanabe, David R. Mortensen
arXiv:2608. 00722v1 Announce Type: cross Abstract: Language model-based text-to-speech (LM-based TTS) remains vulnerable to speech hallucinations that deviate from the target text.
By Chenlin Liu, Minghui Fang, Zhonghao Bi, Zekai Su, Rong Wang, Jiqing Han
PEDBench is a new benchmark for phonetic error detection (PED) that includes natural recordings of children’s speech errors, scripted error productions by actors, and synthetic error productions generated via phone‑conditioned text‑to‑speech. The benchmark provides high‑quality phonetic annotations verified by trained annotators. Evaluation of 13 state‑of‑the‑art speech models shows that they struggle to reliably detect phonetic errors, even in controlled conditions, and often misclassify correct phonemes as errors. Fine‑tuned speech‑language models perform better on word‑level PED, but a sim‑to‑real domain gap persists.
By Zhonghao Shi, Zackary Rackauckas, Jasmine Yip, Savannah Halpern, Simon Campos, Lauren Brace, Emma Yang, Tiantian Feng, Xuan Shi, Shrikanth Narayanan, Teresa Satterfield, Ying Xu
The paper investigates a failure mode called phonological interference in phoneme‑level multilingual speech models, where models incorrectly impose a single language’s phonology on mixed or unseen language input. Experiments show significant loss of language‑specific phonemes in code‑switched and unseen‑language scenarios, and the authors trace this effect to a low‑dimensional subspace of internal activations. They propose a windowed language estimation (WLE) repair that reduces interference by 34–69% while preserving monolingual performance.
By Moran Yanuka, Raja Giryes, Moris Alper
arXiv:2609.15313v1 Announce Type: cross
Abstract: Autoregressive generation of interleaved text and acoustic tokens is a common approach to spoken-response generation in speech large language models....
By Daxin Tan, Dehua Tao, Chengxi Deng, Hanlin Zhang, Xiao Chen
The paper investigates why automatic speech recognition (ASR) systems sometimes generate fluent text that does not correspond to the input audio, a phenomenon termed hallucination. By examining two independently trained Conformer‑Large models—one using CTC and the other RNN‑T—under conditions of environmental noise and speaker‑background shift, the authors identify the final encoder stage as a critical boundary. Bypassing this final block leads to divergence on almost all utterances, while bypassing earlier blocks has minimal effect; at this stage, representations become compact, the decoder can read the text, and grapheme information becomes explicit, yet the output is garbled or repetitive rather than fluent fabrication. The study thus pinpoints a mechanistic precondition for hallucination—failure to produce adequately grounded output—though it does not fully explain naturally occurring hallucinations, and it highlights a consistent terminal‑stage dependency across decoder families and distribution shifts.
By Hamees Sayed, Apoorv Singh, Kumar Aman, Akshat Mandloi
The study investigates how two computational dimensions—model depth and refinement steps—affect intelligibility and speaker identity in masked-diffusion text‑to‑speech systems. Experiments with 15 models (19–133 M parameters) and up to 16 refinement steps show that refinement improves intelligibility more than identity, with a 1.86× asymmetry that persists even after retraining. Best‑of‑K search can recover identity when refinement fails, and analysis indicates that depth and steps target distinct bottlenecks, requiring separate optimization.
By Nityanand Mathur, Hamees Sayed, Ayush Pratap Singh
BranchShine-CR is a 25‑million‑parameter model that transcribes multilingual speech into the International Phonetic Alphabet (IPA). It uses log‑mel features, a rotary‑position E‑Branchformer encoder, intermediate self‑conditioned CTC, and consistency regularization across augmented views. On 16,646 shared IPApack++ test utterances it achieves a 4.47 % IPA character error rate, a 22.3 % relative improvement over ZIPA‑CTC‑NS and outperforms a similarly sized NeMo Conformer baseline across all 41 language labels.
By Nikhil Navas, Sergio Chevtchenko, Talisson Damiao, Saeed Afshar
The paper introduces UGTPhon, a grapheme-to-phoneme benchmark for user‑generated text in English, Vietnamese, and Korean, and presents a taxonomy for diagnosing pronunciation errors. It shows that existing G2P models and large language models struggle with canonical‑to‑non‑canonical text, with errors up to 66.8 PER points. A compositional G2P approach that uses exact‑match lookup and staged decoding reduces these errors and performs competitively with larger few‑shot LLMs.
By MinJu Jeon, Younghan Park, Han Sung Park, Jong-Hwan Kim, Dong-Jin Kim, Hoyeon Lee