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New Text Injection Method Speeds Speech Recognition Training

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for training speech recognition models using unpaired text, which speeds up training by 1.8 times compared to previous approaches. This technique, called "Text Injection," replaces a frozen text-to-speech (TTS) model with a simple lookup table that uses mean log-mel vectors per phoneme. The system achieves state-of-the-art word error rates on the LibriSpeech dataset when combined with an external language model. The researchers have also made the code for the entire pipeline publicly available. AI

IMPACT This method could lead to faster and more efficient training of speech recognition models.

RANK_REASON The cluster contains a research paper detailing a new method for speech recognition.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Text Injection Method Speeds Speech Recognition Training

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The cluster contains a research paper detailing a new method for speech recognition.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Albert Zeyer, Davyd Naveriani, Dorian Koch, Ralf Schl\"uter, Hermann Ney ·

    One Mean Log-Mel Vector per Phoneme: Simple and Fast Text Injection for Speech Recognition

    arXiv:2610.34623v1 Announce Type: new Abstract: Text injection trains speech recognition models on unpaired text alongside paired audio. We feed the speech encoder synthetic input derived from text so the whole model learns from it. Previously, we synthesized this input with a fr…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Hermann Ney ·

    One Mean Log-Mel Vector per Phoneme: Simple and Fast Text Injection for Speech Recognition

    Text injection trains speech recognition models on unpaired text alongside paired audio. We feed the speech encoder synthetic input derived from text so the whole model learns from it. Previously, we synthesized this input with a frozen text-to-speech (TTS) model. Here, a simple …

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