Tag: NLP

How to train your AI on important Keywords | Vapi Tutorial shows you how to eliminate misrecognition of brand names, personal names, and other crucial keywords that often trip up voice assistants. You’ll follow a hands-on walkthrough using Deepgram’s keyword boosting and the Vapi platform to make recognition noticeably more reliable.

First you’ll identify problematic terms, then apply Deepgram’s keyword boosting and set up Vapi API calls to update your assistant’s transcriber settings so it consistently recognizes the right names. This tutorial is ideal for developers and AI enthusiasts who want a practical, step-by-step way to improve voice assistant accuracy and consistency.

Understanding the problem of keyword misinterpretation

You rely on voice AI to capture critical words — brand names, people’s names, product SKUs — but speech systems don’t always get them right. Understanding why misinterpretation happens helps you design fixes that actually work, rather than guessing and tweaking blindly.

Why voice assistants and ASR models misrecognize brand names and personal names

ASR models are trained on large corpora of everyday speech and common vocabularies. Rare or new words, unusual phonetic patterns, and domain-specific terms often fall outside that training distribution. You’ll see errors when a brand name or personal name has unusual spelling, non-standard phonetics, or shares sounds with many more frequent words. Background noise, accents, speaking rate, and recording quality further confuse the acoustic model, while the language model defaults to the most statistically likely tokens, not the niche tokens you care about.

How misinterpretation impacts user experience, automation flows, and analytics

Misrecognition breaks the user experience in obvious and subtle ways. Your assistant might route a call incorrectly, fail to fill an order, or ask for repeated clarification — frustrating users and wasting time. Automation flows that depend on accurate entity extraction (like CRM updates, fulfillment, or account lookups) will fail or create bad downstream state. Analytics and business metrics suffer because your logs don’t reflect true intent or are littered with incorrect keyword transcriptions, masking trends and making A/B testing unreliable.

Types of keywords that commonly break speech recognition accuracy

You’ll see trouble with brand names, personal names (especially uncommon ones), product SKUs and serial numbers, technical jargon, abbreviations and acronyms, slang, and foreign-language words appearing in primarily English contexts. Homophones and short tokens (e.g., “Vapi” vs “vape” vs “happy”) are especially prone to confusion. Even punctuation-sensitive tokens like “A-B-123” can be mis-parsed or merged incorrectly.

Examples from the Vapi tutorial video showing typical failures

In the Vapi tutorial, the presenter demonstrates common failures: the brand name “Vapi” being transcribed as “vape” or “VIP,” “Jannis” being misrecognized as “Janis” or “Dennis,” and product codes getting fragmented or merged. You also observe cases where the assistant drops suffixes or misorders multiword names like “Jannis Moore” becoming just “Moore” or “Jannis M.” These examples highlight how both single-token and multi-token entities can be mishandled, and how those errors ripple through intent routing and analytics.

How to measure baseline recognition errors before applying fixes

Before you change anything, measure the baseline. Collect a representative set of utterances containing your target keywords, then compute metrics like keyword recognition rate (percentage of times a keyword appears correctly in the transcript), word error rate (WER), and slot/entity extraction accuracy. Build a confusion matrix for frequent misrecognitions and log confidence scores. Capture audio conditions (mic type, SNR, accent) so you can segment performance by context. Baseline measurement gives you objective criteria to decide whether boosting or other techniques actually improve things.

Planning your keyword strategy

You can’t boost everything. A deliberate strategy helps you get the most impact with the least maintenance burden.

Defining objectives: recognition accuracy, response routing, entity extraction

Start by defining what success looks like. Are you optimizing for raw recognition accuracy of named entities, correct routing of calls, reliable slot filling for automated fulfillment, or accurate analytics? Each objective influences which keywords to prioritize and which downstream behavior changes you’ll accept (e.g., more false positives vs. fewer false negatives).

Prioritizing keywords by business impact and frequency

Prioritize keywords by a combination of business impact and observed frequency or failure rate. High-value keywords (major product lines, top clients’ names, critical SKUs) should get top priority even if they’re infrequent. Also target frequent failure cases that cause repeated friction. Use Pareto thinking: fix the 20% of keywords that cause 80% of the pain.

Deciding on update cadence and governance for keyword lists

Set a cadence for updates (weekly, biweekly, or monthly) and assign owners: who can propose keywords, who approves boosts, and who deploys changes. Governance prevents list bloat and conflicting boosts. Use change control with versioning and rollback plans so you can revert if a change hurts performance.

Mapping keywords to intents, slots, or downstream actions

Map each keyword to the exact downstream effect you expect: which intent should fire if that keyword appears, which slot should be filled, and what automation should run. This mapping ensures that improving recognition has concrete value and avoids boosting tokens that aren’t used by your flows.

Balancing specificity with maintainability to avoid overfitting

Be specific enough that boosting helps the model pick your target term, but avoid overfitting to very narrow forms that prevent generalization. For example, you might boost the canonical brand name plus common aliases, but not every possible misspelling. Keep the list maintainable and monitor for over-boosting that causes false positives in unrelated contexts.

Collecting and curating important keywords

A great keyword list starts with disciplined discovery and thoughtful curation.

Sources for keyword discovery: transcripts, call logs, marketing lists, product catalogs

Mine your existing data: historical transcripts, call logs, support tickets, CRM entries, and marketing/product catalogs are goldmines. Look at error logs and NLU failure cases for common misrecognitions. Talk to customer-facing teams to surface words they repeatedly spell out or correct.

Including brand names, product SKUs, personal names, technical terms, and abbreviations

Collect brand names, product SKUs and model numbers, personal and agent names, technical terms, industry abbreviations, and location names. Don’t forget accented or locale-specific forms if you operate internationally. Include both canonical forms and common short forms used in speech.

Cleaning and normalizing collected terms to canonical forms

Normalize entries to canonical forms you’ll use downstream for routing and analytics. Decide on a canonical display form (how you’ll store the entity in your database) and record variants and aliases separately. Normalize casing, strip extraneous punctuation, and unify SKU formatting where possible.

Organizing keywords into categories and metadata (priority, pronunciation hints, aliases)

Organize keywords into categories (brand, person, SKU, technical) and attach metadata: priority, likely pronunciations, locale, aliases, and notes about context. This metadata will guide boosting strength, phonetic hints, and testing plans.

Versioning and storing keyword lists in a retrievable format (JSON, CSV, database)

Store keyword lists in version-controlled formats like JSON or CSV, or keep them in a managed database. Include schema for metadata and a changelog. Versioning lets you roll back experiments and trace when changes impacted performance.

Preparing pronunciation variants and aliases

You’ll improve recognition faster if you anticipate how people say the words.

Why multiple pronunciations and spellings improve recognition

People pronounce the same token differently depending on accent, speed, and emphasis. Recording and supplying multiple pronunciations or spellings helps the language model match the audio to the correct token instead of defaulting to a frequent near-match.

Generating likely phonetic variants and common misspellings

Create phonetic variants that reflect likely pronunciations (e.g., “Vapi” -> “Vah-pee”, “Vape-ee”, “Vape-eye”) and common misspellings people might use in typed forms. Use your call logs to see actual misrecognitions and generate patterns from there.

Using aliases, nicknames, and locale-specific variants

Add aliases and nicknames (e.g., “Jannis” -> “Jan”, “Janny”) and locale-specific forms (e.g., “Mercedes” pronounced differently across regions). This helps the system accept many valid surface forms while mapping them to your canonical entity.

When to add explicit phonetic hints vs. relying on boosting

Use explicit phonetic hints when the token is highly unusual or when you’ve tried boosting and still see errors. Boosting increases the prior probability of a token but doesn’t change how it’s phonetically modeled; phonetic hints help the acoustic-to-token matching. Start with boosting for most cases and add phonetic hints for stubborn failures.

Documenting variant rules for future contributors and QA

Document how you create variants, which locales they target, and accepted formats. This lowers onboarding friction for new contributors and provides test cases for QA.

Deepgram keyword boosting overview

Deepgram’s keyword boosting is a pragmatic tool to nudge the ASR model toward your important tokens.

What keyword boosting means and how it influences the ASR model

Keyword boosting increases the language model probability of specified tokens or phrases during transcription. It biases the ASR output toward those terms when the acoustic evidence is ambiguous, making it more likely that your brand names or SKUs appear correctly.

When boosting is appropriate vs. other techniques (custom language models, grammar hints)

Use boosting for quick wins on a moderate set of terms. For highly specialized domains or broad vocabulary shifts, consider custom language models or grammar-based approaches that reshape the model more deeply. Boosting is faster to iterate and less invasive than retraining models.

Typical parameters associated with keyword boosting (keyword list, boost strength)

Typical parameters include the list of keywords (and aliases), per-keyword boost strength (a numeric factor), language/locale, and sometimes flags for exact matching or display form. You’ll tune boost strength empirically — too low has no effect, too high can cause false positives.

Expected outcomes and limitations of boosting

Expect improved recognition for boosted tokens in many contexts, but not perfect results. Boosting doesn’t fix acoustic mismatches (noisy audio, strong accent without phonetic hint) and can increase false positives if boosts are too aggressive or ambiguous. Monitor and iterate.

How boosting interacts with language and acoustic models

Boosting primarily modifies the language modeling prior; the acoustic model still determines how sounds map to candidate tokens. Boosting can overcome small acoustic ambiguity but won’t help if the acoustic evidence strongly contradicts the boosted token.

Vapi platform overview and its role in the workflow

Vapi acts as the orchestration layer that makes boosting and deployment manageable across your assistants.

How Vapi acts as the orchestration layer for voice assistant integrations

You use Vapi to centralize configuration, route audio to transcription services, and coordinate downstream assistant logic. Vapi becomes the single source of truth for transcriber settings and keyword lists, enabling consistent behavior across projects.

Where transcriber settings live within a Vapi assistant configuration

Transcriber settings live in the assistant configuration inside Vapi, usually under a transcriber or speech-recognition section. This is where you set language, locale, and keyword-boosting parameters so that the assistant’s transcription calls include the correct context.

How Vapi coordinates calls to Deepgram and your assistant logic

Vapi forwards audio to Deepgram (or other providers) with the specified transcriber settings, receives transcripts and metadata, and then routes that output into your NLU and business logic. It can enrich transcripts with keyword metadata, persist logs, and trigger downstream actions.

Benefits of using Vapi for fast iteration and centralized configuration

By centralizing configuration, Vapi lets you iterate quickly: update the keyword list in one place and have changes propagate to all connected assistants. It also simplifies governance, testing, and rollout, and reduces the risk of inconsistent configurations across environments.

Examples of Vapi use cases shown in the tutorial video

The tutorial demonstrates updating the assistant’s transcriber settings via Vapi to add Deepgram keyword boosts, then exercising the assistant with recorded audio to show improved recognition of “Vapi” and “Jannis Moore.” It highlights how a single API change in Vapi yields immediate improvements across sessions.

Setting up credentials and authentication

You need secure access to both Deepgram and Vapi APIs before making changes.

Obtaining API keys or tokens for Deepgram and Vapi

Request API keys or service tokens from your Deepgram account and your Vapi workspace. These tokens authenticate requests to update transcriber settings and to send audio for transcription.

Best practices for securely storing keys (env vars, secrets manager)

Store keys in environment variables, managed secrets stores, or a cloud secrets manager — never hard-code them in source. Use least privilege: create keys scoped narrowly for the actions you need.

Scopes and permissions needed to update transcriber settings

Ensure the tokens you use have permissions to update assistant configuration and transcriber settings. Use role-based permissions in Vapi so only authorized users or services can modify production assistants.

Rotating credentials and audit logging considerations

Rotate keys regularly and maintain audit logs for configuration changes. Vapi and Deepgram typically provide logs or you should capture API calls in your CI/CD pipeline for traceability.

Testing credentials with simple read/write API calls before large changes

Before large updates, test credentials with safe read and small write operations to validate access. This avoids mid-change failures during a production update.

Updating transcriber settings with API calls

You’ll send well-formed API requests to update keyword boosting.

General request pattern: HTTP method, headers, and JSON body structure

Typically you’ll use an authenticated HTTP PUT or PATCH to the assistant configuration endpoint with JSON content. Include Authorization headers with your token, set Content-Type to application/json, and craft the JSON body to include language, locale, and keyword arrays.

What to include in the payload: keyword list, boost values, language, and locale

The payload should include your keywords (with aliases), per-keyword boost strength, the language/locale for context, and any flags like exact match or phonetic hints. Also include metadata like version or a change note for your changelog.

Example payload structure for adding keywords and boost parameters

Here’s an example JSON payload structure you might send via Vapi to update transcriber settings. Exact field names may differ in your API; adapt to your platform schema.

{ “transcriber”: { “language”: “en-US”, “locale”: “en-US”, “keywords”: [ { “text”: “Vapi”, “boost”: 10, “aliases”: [“Vah-pee”, “Vape-eye”], “display_as”: “Vapi” }, { “text”: “Jannis Moore”, “boost”: 8, “aliases”: [“Jannis”, “Janny”, “Moore”], “display_as”: “Jannis Moore” }, { “text”: “PRO-12345”, “boost”: 12, “aliases”: [“PRO12345”, “pro one two three four five”], “display_as”: “PRO-12345” } ] }, “meta”: { “changed_by”: “your-service-or-username”, “change_note”: “Add key brand and product keywords” } }

Using Vapi to send the API call that updates the assistant’s transcriber settings

Within Vapi you’ll typically call a configuration endpoint or use its SDK/CLI to push this payload. Vapi then persists the new transcriber settings and uses them on subsequent transcription calls.

Validating the API response and rollback plan for failed updates

Validate success by checking HTTP response codes and the returned configuration. Run a quick smoke transcription test to confirm the changes. Keep a prior configuration snapshot so you can roll back quickly if the new settings cause regressions.

Integrating boosted keywords into your voice assistant pipeline

Boosted transcription is only useful if you pass and use the results correctly.

Flow: capture audio, transcribe with boosted keywords, run NLU, execute action

Your pipeline captures audio, sends it to Deepgram via Vapi with the boosting settings, receives a transcript enriched with keyword matches and confidence scores, sends text to NLU for intent/slot parsing, and executes actions based on resolved intents and filled slots.

Passing recognized keyword metadata downstream for intent resolution

Include metadata like matched keyword id, confidence, and display form in your NLU input so downstream logic can make informed decisions (e.g., exact match vs. fuzzy match). This improves routing robustness.

Handling partial matches, confidence scores, and fallback strategies

Design fallbacks: if a boosted keyword is low-confidence, ask a clarification question, provide a verification step, or use alternative matching (e.g., fuzzy SKU match). Use thresholds to decide when to trust an automated action versus requiring human verification.

Using boosted recognition to improve entity extraction and slot filling

When a boosted keyword is recognized, populate your slot values directly with the canonical display form. This reduces parsing errors and allows automation to proceed without extra normalization steps.

Logging and tracing to link recognition events back to keyword updates

Log which keyword matched, confidence, audio ID, and the transcriber version. Correlate these logs with your keyword list versions to evaluate whether a recent change caused improvement or regression.

Conclusion

You now have an end-to-end approach to strengthen your AI’s recognition of important keywords using Deepgram boosting with Vapi as the orchestration layer. Start by measuring baseline errors, prioritize what matters, collect and normalize keywords, prepare pronunciation variants, and apply boosting thoughtfully. Use Vapi to centralize and deploy configuration changes, keep credentials secure, and validate with tests.

Next steps for you: collect the highest-impact keywords from your logs, create a prioritized list with aliases and metadata, push a conservative boosting update via Vapi, and run targeted tests. Monitor metrics and iterate: tweak boost strengths, add phonetic hints for stubborn cases, and expand gradually.

For long-term success, establish governance, automate collection and testing where possible, and keep involving customer-facing teams to surface new words. Small, well-targeted boosts often yield outsized improvements in user experience and reduced friction in automation flows.

Keep iterating and measuring — with careful planning, you’ll see measurable gains that make your assistant feel far more accurate and reliable.

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