arXiv:2607.06831v1 Announce Type: new Abstract: Speech-to-text alignment means finding the temporal boundaries of each word in the audio. Some models provide such an alignment directly and others do not. Connectionist temporal classification (CTC) and transducer models have an alignment by construction, whereas attention-based encoder-decoders (AED) and speech large language models (LLMs) do not, and their word timings are usually read off the attention weights instead. All of these signals live on the encoder frame grid, which bounds their temporal precision. We study a generic gradient-based
The proliferation of various ASR models, especially specialized Speech LLMs, necessitates more robust and unified methodologies for speech-to-text alignment.
This development improves a fundamental capability for speech processing, enhancing the accuracy and utility of diverse ASR systems across many applications.
A generic gradient-based approach allows for consistent and more precise word timing alignment, independent of the specific ASR architecture, including those that previously lacked direct alignment mechanisms.
- · ASR developers
- · Speech LLM users
- · Voice assistant providers
- · Transcription services
More accurate and versatile speech-to-text alignment tools become available to researchers and developers.
Improved indexing, search, and accessibility features for audio and video content become possible due to enhanced temporal precision.
The democratization of high-quality speech processing could accelerate the development of complex interactive AI agents and multimodal systems.
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