Large-scale Language Model Rescoring on Long-form Data
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.
648bed8a4b8596d017f16759
Tongzhou Chen
648bed8a4b8596d017f1675a
Cyril Allauzen
648bed8a4b8596d017f1675b
Yinghui Huang
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Daniel Park
648bed8a4b8596d017f1675d
David Rybach
648bed8a4b8596d017f1675e
W. Ronny Huang
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Rodrigo Cabrera
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Kartik Audhkhasi
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Bhuvana Ramabhadran
648bed8a4b8596d017f16762
Pedro J. Moreno
648bed8a4b8596d017f16763
Michael Riley
Large-scale Language Model Rescoring on Long-form Data
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.
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.
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