Domain Adaptation of Automatic Speech Recognition Models for Diagnostic Applications

Abstract

Automatic speech recognition (ASR), or speech-to-text (STT), is becoming an important interface for AI systems in diagnostic workflows, but general-purpose ASR models often degrade in specialized technical domains. In diagnostic applications such as fault identification, root cause analysis, and repair recommendation, general-purpose ASR systems struggle with domain-specific terminology, abbreviations, part identifiers, and measurement expressions, leading to elevated transcription errors. This work presents a domain adaptation pipeline that unifies three components: a synthetic benchmarking framework in which domain-specific technical text is converted to speech via text-to-speech~(TTS) synthesis and transcribed by open-source ASR models to establish baseline performance; Low-Rank Adaptation~(LoRA)-based fine-tuning of Whisper Large-v3 using those synthetic audio-text pairs; and transfer validation on curated real-world automotive YouTube recordings to assess generalization beyond synthetic conditions. Using automotive technical language as a representative diagnostic domain, a data-scaling study employing progressively larger subsets of in-domain training data evaluates performance on a held-out test set via word error rate~(WER), character error rate~(CER), normalized error metrics, alphanumeric error rate, semantic similarity, and Bidirectional Encoder Representations from Transformers Score~(BERTScore). Results show consistent gains from lightweight domain adaptation on both held-out synthetic data and real-world recordings, confirming that synthetic data generation combined with LoRA-based fine-tuning is an effective and computationally practical strategy for improving ASR accuracy in specialized technical domains where labeled speech is scarce.