What Developers Should Know About Model Selection, Adaptation, and Tuning for

Enterprises that utilize speech AI technologies face challenges in selecting, adapting, and fine-tuning speech-to-text (STT) models to accurately transcribe domain-specific vocabulary. Despite advancements in speech AI, models like Nova-3 and Whisper, which have been trained on broad audio sources, often struggle with specialized terms that are crucial for specific industries such as medicine or finance. Key metrics for evaluating STT model performance include Word Error Rate (WER), Keyword Recall Rate (KRR), Character Error Rate (CER), and Real-Time Factor (RTF). These metrics help distinguish between general model accuracy and performance on critical domain-specific terms. To improve model performance on niche vocabulary, developers can adapt models using domain-specific data and fine-tune pretrained models. Analyzing enterprise audio using techniques like Term Frequency-Inverse Document Frequency (TF-IDF) can identify important domain terms that are underrepresented in general STT models. Ultimately, understanding and applying these metrics and adaptation techniques enable businesses to select the most effective STT models for their unique audio data needs.

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