title: Text-only Domain Adaptation using Unified Speech-Text Representation in
    Transducer
summary: Domain adaptation using text-only corpus is challenging in end-to-end(E2E)
  speech recognition. Adaptation by synthesizing audio from text through TTS is
  resource-consuming. We present a method to learn Unified Speech-Text
  Representation in Conformer Transducer(USTR-CT) to enable fast domain
  adaptation using the text-only corpus. Different from the previous textogram
  method, an extra text encoder is introduced in our work to learn text
  representation and is removed during inference, so there is no modification for
  online deployment. To improve the efficiency of adaptation, single-step and
  multi-step adaptations are also explored. The experiments on adapting
  LibriSpeech to SPGISpeech show the proposed method reduces the word error
  rate(WER) by relatively 44% on the target domain, which is better than those of
  TTS method and textogram method. Also, it is shown the proposed method can be
  combined with internal language model estimation(ILME) to further improve the
  performance.
paper_authors._id: 64812e0f9860cd75c260b564
paper_authors.name: Lu Huang
paper_authors._id: 64812e0f9860cd75c260b565
paper_authors.name: Boyu Li
paper_authors._id: 64812e0f9860cd75c260b566
paper_authors.name: Jun Zhang
paper_authors._id: 64812e0f9860cd75c260b567
paper_authors.name: Lu Lu
paper_authors._id: 64812e0f9860cd75c260b568
paper_authors.name: Zejun Ma
paper_title: Text-only Domain Adaptation using Unified Speech-Text Representation in
    Transducer
paper_summary: Domain adaptation using text-only corpus is challenging in end-to-end(E2E)
  speech recognition. Adaptation by synthesizing audio from text through TTS is
  resource-consuming. We present a method to learn Unified Speech-Text
  Representation in Conformer Transducer(USTR-CT) to enable fast domain
  adaptation using the text-only corpus. Different from the previous textogram
  method, an extra text encoder is introduced in our work to learn text
  representation and is removed during inference, so there is no modification for
  online deployment. To improve the efficiency of adaptation, single-step and
  multi-step adaptations are also explored. The experiments on adapting
  LibriSpeech to SPGISpeech show the proposed method reduces the word error
  rate(WER) by relatively 44% on the target domain, which is better than those of
  TTS method and textogram method. Also, it is shown the proposed method can be
  combined with internal language model estimation(ILME) to further improve the
  performance.
paper_ai_keywords: Unified Speech-Text Representation in Conformer Transducer (USTR-CT) text encoder domain adaptation text-only corpus textogram method Conformer Transducer LibriSpeech SPGISpeech word error rate (WER) internal language model estimation (ILME)