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The article surveys brain‑to‑language decoding, covering tasks, neural signals, methods, and evaluation across invasive and non‑invasive modalities. It traces the field’s evolution from constrained recognition to text generation, streaming speech, and facial animation, linking tasks to neural populations and decoder representations. The review highlights complementary decoding targets, shared representations, and the growing importance of calibration, feedback, and user control for online communication, while proposing a five‑level future trajectory toward bidirectional cognitive exchange.
arXiv:2609.36736v1 Announce Type: cross Abstract: Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative in...
arXiv:2610.34457v1 Announce Type: cross Abstract: Speech and language technologies have expanded access to communication, but the support they provide is often fixed. The same linguistic or visual ai...
The paper introduces Language as Supervision (LaS), a method that aligns neural activity with natural‑language class descriptions via learned projections and uses candidate text prototypes for classification. Evaluated across seven encoder architectures on visual, motor, and speech decoding tasks from scalp and intracranial recordings, LaS boosts speech‑decoding accuracy by up to 17.15 percentage points compared to traditional class‑label supervision. The approach also enables reusable candidate sets, subject‑ and session‑adaptation, and conditional unseen‑frequency decoding in steady‑state visual evoked potentials, with dual‑sided attribution visualizations highlighting both neural and textual contributions to class matching.
The paper introduces Alignment-Free Text‑Audiobox (Text‑AB), a unified diffusion‑based framework that performs high‑quality voice dubbing and full‑duplex dialogue synthesis without requiring forced alignment. Text‑AB uses a latent diffusion model with DAC‑VAE features, achieving over 10× compression compared to prior EnCodec representations, and learns text‑speech alignment via cross‑attention. The authors pretrain a 3B‑parameter model on 480k hours of monolingual speech and fine‑tune it for cross‑lingual dubbing, full‑duplex dialogue, and emotional dialogue, reporting significant improvements in prosody, voice similarity, naturalness, and emotional expressivity over existing internal systems.
arXiv:2606. 07547v1 Announce Type: cross Abstract: Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs.