Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
This is an incremental overview paper that synthesizes existing research for practitioners in speech recognition.
The paper provides a structured overview of adaptation algorithms for neural network-based speech recognition, covering speaker, domain, and accent adaptation, and includes a meta-analysis of performance based on relative error rate reductions from the literature.
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.