title: Large-scale Language Model Rescoring on Long-form Data
summary: In this work, we study the impact of Large-scale Language Models (LLM) on
  Automated Speech Recognition (ASR) of YouTube videos, which we use as a source
  for long-form ASR. We demonstrate up to 8\% relative reduction in Word Error
  Eate (WER) on US English (en-us) and code-switched Indian English (en-in)
  long-form ASR test sets and a reduction of up to 30\% relative on Salient Term
  Error Rate (STER) over a strong first-pass baseline that uses a maximum-entropy
  based language model. Improved lattice processing that results in a lattice
  with a proper (non-tree) digraph topology and carrying context from the 1-best
  hypothesis of the previous segment(s) results in significant wins in rescoring
  with LLMs. We also find that the gains in performance from the combination of
  LLMs trained on vast quantities of available data (such as C4) and conventional
  neural LMs is additive and significantly outperforms a strong first-pass
  baseline with a maximum entropy LM.
paper_authors._id: 648bed8a4b8596d017f16759
paper_authors.name: Tongzhou Chen
paper_authors._id: 648bed8a4b8596d017f1675a
paper_authors.name: Cyril Allauzen
paper_authors._id: 648bed8a4b8596d017f1675b
paper_authors.name: Yinghui Huang
paper_authors._id: 648bed8a4b8596d017f1675c
paper_authors.name: Daniel Park
paper_authors._id: 648bed8a4b8596d017f1675d
paper_authors.name: David Rybach
paper_authors._id: 648bed8a4b8596d017f1675e
paper_authors.name: W. Ronny Huang
paper_authors._id: 648bed8a4b8596d017f1675f
paper_authors.name: Rodrigo Cabrera
paper_authors._id: 648bed8a4b8596d017f16760
paper_authors.name: Kartik Audhkhasi
paper_authors._id: 648bed8a4b8596d017f16761
paper_authors.name: Bhuvana Ramabhadran
paper_authors._id: 648bed8a4b8596d017f16762
paper_authors.name: Pedro J. Moreno
paper_authors._id: 648bed8a4b8596d017f16763
paper_authors.name: Michael Riley
paper_title: Large-scale Language Model Rescoring on Long-form Data
paper_summary: In this work, we study the impact of Large-scale Language Models (LLM) on
  Automated Speech Recognition (ASR) of YouTube videos, which we use as a source
  for long-form ASR. We demonstrate up to 8\% relative reduction in Word Error
  Eate (WER) on US English (en-us) and code-switched Indian English (en-in)
  long-form ASR test sets and a reduction of up to 30\% relative on Salient Term
  Error Rate (STER) over a strong first-pass baseline that uses a maximum-entropy
  based language model. Improved lattice processing that results in a lattice
  with a proper (non-tree) digraph topology and carrying context from the 1-best
  hypothesis of the previous segment(s) results in significant wins in rescoring
  with LLMs. We also find that the gains in performance from the combination of
  LLMs trained on vast quantities of available data (such as C4) and conventional
  neural LMs is additive and significantly outperforms a strong first-pass
  baseline with a maximum entropy LM.
paper_ai_summary: Large-scale language models improve long-form ASR performance on YouTube videos, reducing Word Error Rate and Salient Term Error Rate compared to traditional models.
paper_ai_keywords: Large-scale Language Models (LLM) Automated Speech Recognition (ASR) Word Error Rate (WER) Salient Term Error Rate (STER) long-form ASR lattice processing digraph topology C4 neural LMs