53 citations · 151 across the 26 of their papers we have counts for
8 papers · 1 filter
Discriminative Speech Recognition Rescoring with Pre-trained Language Models
Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yile Gu +3
Second pass rescoring is a critical component of competitive automatic speech recognition (ASR) systems. Large language models have demonstrated their ability in using pre-trained…
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting
Chao-Han Huck Yang, Yile Gu, Yi-Chieh Liu +3
We explore the ability of large language models (LLMs) to act as speech recognition post-processors that perform rescoring and error correction. Our first focus is on instruction p…
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition
Yu Yu, Chao-Han Huck Yang, Jari Kolehmainen +15
We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have s…
Personalization for BERT-based Discriminative Speech Recognition Rescoring
Jari Kolehmainen, Yile Gu, Aditya Gourav +4
Recognition of personalized content remains a challenge in end-to-end speech recognition. We explore three novel approaches that use personalized content in a neural rescoring step…
Scaling Laws for Discriminative Speech Recognition Rescoring Models
Yile Gu, Prashanth Gurunath Shivakumar, Jari Kolehmainen +3
Recent studies have found that model performance has a smooth power-law relationship, or scaling laws, with training data and model size, for a wide range of problems. These scalin…
Distillation Strategies for Discriminative Speech Recognition Rescoring
Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yile Gu +3
Second-pass rescoring is employed in most state-of-the-art speech recognition systems. Recently, BERT based models have gained popularity for re-ranking the n-best hypothesis by ex…
PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers
Rahul Pandey, Roger Ren, Qi Luo +7
End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names an…
On-the-fly Text Retrieval for End-to-End ASR Adaptation
Bolaji Yusuf, Aditya Gourav, Ankur Gandhe +1
End-to-end speech recognition models are improved by incorporating external text sources, typically by fusion with an external language model. Such language models have to be retra…