Post-training (also called fine-tuning) enables enterprises to customize pre-trained speech recognition models for domain-specific requirements without training models from scratch. This approach leverages transfer learning, where knowledge learned from large general-purpose datasets is adapted to specific use cases. The process is significantly more efficient than training from scratch while providing substantial accuracy improvements for specialized domains.
The theoretical foundation of post-training lies in representation learning. Pre-trained models have learned general acoustic and linguistic patterns from thousands of hours of diverse audio. These learned representations capture universal features of human speech: phoneme structure, prosody patterns, common vocabulary, and linguistic relationships. When fine-tuning on domain-specific data, the model adjusts these representations to better handle specialized terminology, accents, or speaking styles while retaining general speech understanding capabilities.
Data preparation is the most critical aspect of successful post-training. High-quality training data requires accurate transcriptions, appropriate audio quality, and representative coverage of target scenarios. We recommend collecting at least 50-100 hours of domain-specific audio for meaningful improvements, though 20-30 hours can provide noticeable benefits. The data should represent the expected production scenarios: same audio quality, similar speaking styles, comparable background noise levels. Mismatched training data (high-quality studio recordings when production uses phone audio) leads to poor generalization.
Transcription accuracy directly impacts model quality. Errors in training transcriptions teach the model incorrect mappings. We recommend human transcription by domain experts familiar with technical terminology. Automated transcription tools can provide initial drafts, but human review is essential. Quality assurance processes should check transcription accuracy, consistency of terminology usage, and proper handling of numbers, names, and specialized vocabulary. Inter-annotator agreement metrics help ensure transcription quality.
Training configuration requires careful hyperparameter selection. Learning rates must balance adapting to new data while avoiding catastrophic forgetting of general capabilities. We typically use learning rates 10-100x smaller than initial training (e.g., 1e-5 to 1e-6). Training duration is determined by validation performance—we monitor WER on held-out validation data and stop when improvements plateau (early stopping). Typically, 5-10 epochs provide optimal results, though this varies with dataset size and domain shift.
Regularization techniques prevent overfitting to limited training data. Dropout maintains generalizability by randomly deactivating neurons during training. Weight decay (L2 regularization) prevents weights from becoming too large. Data augmentation artificially expands the training dataset: adding background noise, varying audio speed (time stretching), and simulating different audio codecs. These techniques effectively increase dataset size 3-5x without collecting additional audio, improving model robustness.
Evaluation methodology must reflect production use cases. Simple WER on test sets doesn't fully capture enterprise requirements. We evaluate on multiple metrics: overall WER, entity-level accuracy (names, numbers, technical terms), sentence-level accuracy, and downstream task performance (like information extraction accuracy). Domain-specific evaluation sets should mirror production scenarios: same audio quality, similar content types, representative speakers. Cross-validation or hold-out validation sets prevent overfitting to test data.
Incremental fine-tuning enables continuous improvement as more data becomes available. Instead of retraining from scratch, models can be incrementally updated with new data. This approach is more efficient and allows models to adapt to changing requirements or expanding use cases. Care must be taken to maintain performance on original tasks while improving on new data—techniques like elastic weight consolidation or experience replay can help prevent catastrophic forgetting.
Deployment considerations include model versioning, A/B testing, and rollback capabilities. Version control tracks model iterations, training data used, and performance metrics. A/B testing infrastructure enables comparing new model versions against production models on live traffic. Gradual rollout (starting with small traffic percentages) reduces risk. Rollback mechanisms allow quick reversion if new models perform poorly. Monitoring production metrics (accuracy, latency, error rates) provides feedback for future improvements.
Cost-benefit analysis helps determine when post-training is worthwhile. The process requires data collection, transcription, computational resources for training, and ongoing maintenance. Benefits include improved accuracy (reducing manual correction costs), better user experience, and domain-specific capabilities. For high-volume use cases processing thousands of hours monthly, even modest accuracy improvements can justify significant investment. Lower-volume use cases may find general-purpose models sufficient, with domain-specific preprocessing or post-processing providing adequate improvements.