The Most Common Pronunciation Errors in TTS (Based on Real Tests)

Platform builders creating voice-powered products for enterprise contact centers handling 500,000+ monthly voice interactions face a consistent problem: pronunciation errors in account numbers, policy IDs, and transaction amounts drive voice agent calls to human escalation. These pronunciation-driven escalations translate to $1.8-2.16M annually in preventable costs, calculated from industry-standard $3-4 per-call escalation costs multiplied by the 15-18% of calls affected by pronunciation failures.

Production deployment data shows that addressing voice agent quality issues, including pronunciation accuracy, contributed to a 70% reduction in missed calls at organizations like Five9, validating the operational impact of these error categories. Platform builders must understand how pronunciation failures cascade through their customer deployments, creating compounding trust issues when voice agents repeatedly mispronounce critical information.

This article identifies five pronunciation error categories from production voice agent deployments, provides testing methodology matched to each category, and delivers fix strategies with specific guidance for entity-aware processing approaches versus industry-standard SSML and lexicon patterns.

• Homograph disambiguation accuracy drops 25-40 percentage points between controlled testing and production deployments due to context window limitations and real-time latency constraints
• Testing methodology requires minimum coverage: 20+ numeric edge cases, 50+ homograph pairs with context variations, and 100% proper noun coverage for domain-specific terms
• Entity-aware TTS processing handles common pronunciation patterns automatically, while SSML markup and custom lexicons offer granular control at the cost of additional complexity

Production voice agent deployments reveal five distinct categories of common TTS pronunciation errors that platform builders must understand when evaluating TTS infrastructure for their enterprise customers.

Homograph disambiguation failures occur when TTS systems mispronounce words with identical spelling but different pronunciations based on context. The word "read" should sound different in "I read books daily" versus "I read that report yesterday."

Interspeech 2024 research documents that homograph errors represent a significant portion of pronunciation failures detectable by humans. Research from Google shows state-of-the-art systems achieve 99.1% accuracy in laboratory conditions, while deployed systems reveal accuracy drops to 60-65% due to real-time latency constraints forcing limited local token windows.

Platform builders should help enterprise customers maintain priority lists of domain-critical homographs. Financial services frequently encounter "close" (verb: terminate position vs. adjective: near) and "minute" (noun: time unit vs. adjective: small). Healthcare contexts add "wound" (noun: injury vs. verb: coiled) and "invalid" (adjective: not valid vs. noun: disabled person).

Alphanumeric strings present unique challenges because TTS systems must determine whether to spell characters individually, group them, or apply specific pacing rules. Order numbers like "ORD-2024-78A3" require consistent pronunciation patterns that customers can verify against confirmation emails or account dashboards.

Testing should include edge cases like leading zeros (account ID "007854"), mixed-case sensitivity ("Order-2024-ABcd"), and delimiter variations (hyphens, underscores, periods). Each format variation requires explicit pronunciation rules to ensure consistent customer experience across thousands of daily interactions.

The string "2025" requires different pronunciation as a year ("twenty twenty-five"), quantity ("two thousand twenty-five"), or confirmation code ("two-zero-two-five"). Currency amounts need proper denomination handling: "$45.99" should sound like "forty-five dollars and ninety-nine cents."

The W3C SSML specification defines the <say-as> element with interpret-as attributes for telephone, date, time, currency, ordinal, and cardinal formats. Voice agents processing transaction confirmations must implement explicit formatting control for each numeric context.

Edge case testing must cover international format variations: European date formats (DD/MM/YYYY vs. MM/DD/YYYY), currency symbols with different decimal conventions (1.234,56 € vs. $1,234.56), and phone number formats across regions.

Proper nouns lack standardized pronunciation rules in English phonology and frequently appear as out-of-vocabulary words in TTS lexicons. Proper noun errors drive escalation requests when customers cannot verify identity or account information.

Effective proper noun coverage requires analysis of deployed data to identify the highest-frequency names in your customer base. A financial services firm may need 200+ customer surnames, while a healthcare provider needs medical professional names, drug brand names, and procedure terminology.

TTS systems struggle to determine whether acronyms should be pronounced as words (NATO, RADAR) or spelled letter-by-letter (FBI, CEO). Healthcare contexts contain medical acronyms like HIPAA and MRI; financial services use regulatory acronyms like APR and IRA.

Industry-specific acronym lists require ongoing maintenance. New regulatory acronyms enter vocabulary regularly (GDPR, CCPA, DORA), and product acronyms evolve with market changes. Establish quarterly review cycles to identify and add pronunciation rules for emerging terminology.

Testing and remediation strategies vary by error category. Platform builders need systematic approaches that match fix techniques to specific pronunciation failure patterns.

For TTS providers supporting SSML, the W3C SSML specification provides the <phoneme> element for word-level pronunciation overrides. Low-frequency edge cases affecting under 0.1% of utterances work well with inline SSML phoneme tags using IPA or X-SAMPA notation.

Testing homograph fixes requires minimum 50+ pairs with 3+ context variations per pair. For "read," test present tense declarative ("I read reports daily"), past tense question ("Did you read the memo?"), and present tense command ("Please read the terms aloud").

For TTS systems using entity-aware processing rather than SSML, implement text preprocessing pipelines that apply phonetic respelling before API calls. Write "Nguyen" as "win," "nuh-WIN," or "new-YEN" depending on regional pronunciation preferences.

The W3C Pronunciation Lexicon Specification (PLS) enables centralized vocabulary management for 50-200+ high-frequency domain terms. Implement versioned lexicon URIs that your development team can update independently of code deployments.

Systems processing over 10,000 requests daily benefit from lexicon architectures where PLS files reduce per-request payload overhead by 10-25% compared to inline SSML markup. Store pronunciation rules in version-controlled configuration files that allow non-technical staff to maintain adjustments as business terminology evolves.

For platforms without lexicon support, implement centralized text preprocessing rules that apply consistent phonetic respelling across all API calls.

Deepgram's text-to-speech includes entity-aware processing that automatically handles addresses, phone numbers, and alphanumeric IDs through embedded language understanding. The system recognizes and correctly pronounces numeric values, date formats, currency amounts, email addresses, and URLs without requiring explicit markup.

Testing numeric formats requires minimum 20+ edge cases covering: international date formats, currency with varying decimal conventions, phone numbers across regions, and alphanumeric IDs with mixed delimiters. Track Word Error Rate (WER) calculated as substitutions plus deletions plus insertions divided by total words, targeting 6-18% for deployed voice agents.

Enterprise deployments processing thousands of daily calls require systematic pronunciation management beyond individual fixes.

Create domain-specific pronunciation libraries covering the terminology your enterprise customers encounter most frequently. Healthcare implementations need medical procedure names, drug brands, and anatomical terms. Financial services require product names, regulatory terminology, and transaction types.

Analyze production logs to identify the 200-500 terms appearing most frequently in customer interactions, then prioritize pronunciation rules for terms with documented failure patterns.

Automated pronunciation testing should run on every code deployment. Configure CI/CD pipelines to synthesize test utterances covering all five error categories and compare audio output against reference recordings using automated quality metrics. Failed tests should block deployment until pronunciation issues are resolved.

Deepgram's Voice Agent API supports integration patterns that apply pronunciation preprocessing at the pipeline level rather than per-request, reducing latency overhead for high-volume deployments.

Implement pronunciation feedback loops that collect user corrections through rating interfaces, store phonetic respellings indexed by term or customer ID, and apply corrections automatically in future interactions. Systems tracking correction patterns can identify systematic issues requiring broader pronunciation rule updates.

Regression testing at 500+ conversation paths means testing complete user interaction flows, not just individual utterances. A banking voice agent should test complete flows: account balance inquiry (5 numeric format tests), transfer confirmation (alphanumeric ID + currency), and identity verification (proper name + date of birth).

Match your remediation strategy to error category and deployment scale:

• Homographs and proper names: Text preprocessing with phonetic respelling for entity-aware systems; SSML phoneme tags for markup-based systems
• Domain vocabulary (50-200+ terms): Centralized preprocessing rules or PLS lexicons depending on provider support
• Numbers and entities: Entity-aware processing handles common patterns automatically; SSML say-as elements for edge cases requiring explicit format control
• Acronyms: Maintain versioned acronym lists with quarterly review cycles; apply spacing techniques ("C E O") or alias definitions

Platform builders evaluating TTS infrastructure should test pronunciation accuracy with domain-specific content before committing to production deployments.

Start testing with Deepgram Console and $200 in free credits to validate pronunciation handling across your enterprise customers' terminology requirements.

Establish baseline metrics by sampling 1,000+ production utterances and calculating category-specific error rates before implementing fixes. Track improvements weekly using automated STT verification that compares synthesized audio against expected transcriptions. Set category-specific targets: homograph accuracy above 95%, entity pronunciation consistency above 98%, and proper name recognition above 90% for high-frequency surnames.

Configure pre-deployment gates that synthesize 100+ test utterances spanning all five error categories, with weighted sampling toward recently-modified pronunciation rules. Include regression tests for previously-fixed issues and negative tests for known failure patterns from production logs. Block deployments when any category falls below threshold accuracy until issues are resolved.

Analyze production escalation data to identify which error categories drive the highest customer impact. Typically, proper name mispronunciations and alphanumeric entity errors cause immediate escalation requests, while homograph errors create cumulative trust erosion. Fix high-frequency, high-impact terms first: the 50 most common customer surnames, critical product identifiers, and transaction confirmation formats that appear in 80%+ of calls.