Teaching AI to Speak Your Industry: Training Voice Agents on Domain-Specific Terminology

Making an AI voice or chat agent “speak your language” is less about a single model and more about a layered approach across speech recognition, language understanding, retrieval, and speech synthesis. The goal is consistent recognition, correct meaning, and compliant usage of domain terms in real conversations.

What “understand terminology” really means

1. Hear it: ASR correctly recognizes brand names, acronyms, model numbers, and jargon.
2. Mean it: NLU/LLM maps terms to the right concepts (entities, intents) and actions.
3. Say it: TTS pronounces terms properly and uses preferred phrasing.
4. Use it: The agent applies terms consistently with policy and context (e.g., regulatory language, disclaimers).

Data sources to use

1. Your documents: product catalogs, manuals, SOPs, knowledge base, FAQs, policy docs.
2. Live data: CRM/ERP fields, ticketing notes, past chats/calls (de-identified).
3. Glossaries: official terminology, synonyms, abbreviations, and banned terms.
4. Ontologies: category hierarchies, SKU/part trees, ICD/LOINC/FIN codes where relevant.
5. Change feeds: release notes, price lists, regulatory updates.

Layered adaptation: how to teach each component

1. Speech recognition (ASR)
2. Custom vocabulary and phrase boosting: inject domain words (SKUs, drug names, alphanumerics) and increase their likelihood.
3. Class-based grammars: patterns for IDs, serials, dates, amounts (e.g., <MODEL_NUMBER>, <POLICY_ID>).
4. Continuous updates: add newly observed terms from transcripts to the vocabulary (with review).
5. Language understanding and dialogue (LLM/NLU)
6. Retrieval-Augmented Generation (RAG): ground the model on your knowledgebase, policies, and catalogs at answer time; prevents drift and reduces hallucinations.
7. Glossary-driven normalization: map synonyms and acronyms to canonical terms (e.g., “HTN” → “hypertension”, “prem” → “premium”).
8. Slot/entity schemas: define fields the agent must capture (product_code, dosage, coverage_type), with validators and regex where needed.
9. Few-shot prompting: include labeled examples showing correct use of terms, disambiguation, and style.
10. Supervised fine-tuning or adapters: train on de-identified past interactions and synthetic variants to improve intent/slot accuracy for your domain.
11. Tool use: teach the agent to call internal APIs for authoritative answers (inventory, eligibility) rather than guessing.
12. Text-to-speech (TTS)
13. Pronunciation lexicon: add IPA/phoneme entries for brand names, chemicals, and model IDs to avoid mispronunciation.
14. Prosody controls: ensure disclaimers and legal phrases are delivered clearly and consistently.
15. Language variants: pick voices and styles that match your brand and user demographics.

Curation and governance workflow

1. Collection: ingest documents, transcripts, and catalogs; de-identify PII to meet GDPR/HIPAA/PCI as needed.
2. Glossary management: owners approve canonical terms, synonyms, and forbidden phrases; version and audit changes.
3. Labeling: annotate intents, entities, and outcomes on a stratified sample (include edge cases and errors).
4. Augmentation: generate realistic paraphrases, accents, and noise-mixed audio to broaden coverage.
5. Reviews: domain experts validate outputs; legal/compliance approve sensitive phrasing.

Evaluation and acceptance criteria

1. Entity/slot F1 for critical fields (names, IDs, amounts, codes) ≥ 95% in clean tests; ≥ 90% in noisy/telephony tests.
2. Intent accuracy and task success rate across top journeys.
3. ASR word error rate and, more importantly, key-term accuracy under accents/noise.
4. Groundedness: percent of answers citing KB or tool outputs.
5. Safety/compliance checks: zero use of banned terms; correct disclaimers.

Continuous learning in production

1. Shadow mode: test new vocab and prompts on live traffic without affecting answers.
2. Feedback loop: capture corrections from agents/customers; convert to training data.
3. Drift detection: monitor new terms, product changes, and policy updates; auto-flag retraining needs.
4. Scheduled refresh: monthly glossary updates, quarterly prompt/fine-tune updates, ongoing RAG indexing.

Best practices that move the needle

1. Start with RAG + glossary normalization before model fine-tuning.
2. Use constrained grammars for critical fields (policy numbers, amounts) to reduce errors.
3. Confirm high-stakes terms with the user (“Did you say plan ‘Silver Plus’?”).
4. Prefer authoritative tools/APIs over model memory for prices, coverage, inventory.
5. Keep prompts short and explicit; show canonical terminology in the few-shot examples.
6. Maintain a single source of truth for terms and synonyms; automate propagation to ASR, LLM, and TTS.

Quick checklist

1. Provide your glossary, forbidden terms, and preferred style guide.
2. Share top 20 intents and 200–1,000 real examples per intent if available.
3. Supply product/catalog exports and API access for source-of-truth queries.
4. Approve a test plan with target metrics (entity F1, task success, groundedness).
5. Set a monthly cadence for glossary and model updates.

Training an AI agent on your industry terminology is a systematic, multi-layer process: tune ASR to hear your words, teach the LLM to mean them, ground answers in your sources, and ensure TTS says them right. With solid data, governance, and continuous learning, the agent becomes a reliable, compliant expert in your domain.