arXiv:2008.06580
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
원문 초록
We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation. The overview characterizes adaptation algorithms as based on embeddings, model parameter adaptation, or data augmentation. We present a meta-analysis of the performance of speech recognition adaptation algorithms, based on relative error rate reductions as reported in the literature.
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Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models
BUT System for the MLC-SLM Challenge
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
LLM-based phoneme-to-grapheme for phoneme-based speech recognition
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