StreamAlign: Streaming Text-Aligned Speech Tokenization
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretrained LLMs. However, they rely on offline automat...
arXiv:2607. 06831v1 Announce Type: cross Abstract: Speech-to-text alignment means finding the temporal boundaries of each word in the audio.
arXiv:2506. 16738v2 Announce Type: replace-cross Abstract: With the rapid progress of speech language models (SLMs), discrete speech tokens have emerged as a core interface between speech and text, enabling unified modeling across modalities.
arXiv:2602.19574v2 Announce Type: replace-cross Abstract: Large-language-model (LLM)-based text-to-speech (TTS) systems can generate natural speech, but most are not designed for low-latency dual-str...
arXiv:2609.14991v1 Announce Type: new Abstract: Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling ou...
The paper introduces Local Prototype Reconstruction (LPR), a lightweight regularizer that encourages bridge embeddings in speech‑to‑LLM systems to stay close to the frozen LLM’s input‑embedding neighbourhoods. By measuring lexical manifold compatibility with a fixed, head‑free, timestamp‑free diagnostic, the authors show that next‑word prediction and sentence‑level contrastive pretraining do not fully capture token‑level lexical compatibility. Experiments on multilingual ASR and speech translation demonstrate that LPR improves transfer, especially for translation and low‑resource adaptation, and that the diagnostic correlates with downstream gains across objectives.