Transfer Learning in Speech Recognition: Dev Guide

Transfer learning adapts pre-trained automatic speech recognition models to specialized vocabulary, languages, accents, and recording conditions, potentially reducing word error rates by 20–99% compared with generic systems that often misrecognize medical, financial, technical, or proprietary terms. The approach ranges from instant runtime vocabulary tools such as keyword boosting or keyterm prompting, generally suited to limited term lists, to managed custom-model services and self-hosted fine-tuning with models such as Whisper, NeMo, and wav2vec 2.0. Data quality is emphasized as a major determinant of outcomes: useful improvements may begin with about one hour of accurately labeled audio, while large production deployments can benefit from hundreds of hours, with diminishing returns near 860 hours. Customization can improve domain accuracy and reduce manual correction costs, but it also risks catastrophic forgetting, in which a model’s performance on general speech declines substantially unless techniques such as experience replay are used. Organizations are advised to measure baseline word and slot error rates on representative audio, test customized models against baselines, account for latency and compliance needs, and choose among API-based, self-hosted, or lightweight vocabulary adaptation according to infrastructure, data-residency, timeline, and accuracy requirements.

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