Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

Abstract

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.

Total of 31 pages, 27 figures. Associated repository: https://github.com/pswietojanski/ojsp_adaptation_review_2020

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Cited by in corpus (8)

• Self-Supervised Speech Representation Learning: A Review
• Generalizing Speaker Verification for Spoof Awareness in the Embedding Space
• A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data
• TS-Net: OCR Trained to Switch Between Text Transcription Styles
• On Addressing Practical Challenges for RNN-Transducer
• Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition
• Seed Words Based Data Selection for Language Model Adaptation
• Efficient Extraction of Noise-Robust Discrete Units from Self-Supervised Speech Models