AI Voice Quality Checklist: Pronunciation, Pace, Breath, and Consistency

Evaluating TTS quality is subjective—but it doesn't have to be arbitrary. This checklist gives you a systematic way to assess AI voices, whether you're comparing providers, reviewing a voice actor's synthetic clone, or quality-checking your own content.

Pronunciation

Word-level accuracy

• Common words pronounced correctly
• Proper nouns handled appropriately (or flags for SSML/lexicon—see our SSML guide for fixing problem words)
• Acronyms expanded or spelled out correctly
• Numbers read in context-appropriate format
• Foreign words/loanwords handled consistently

Sound quality

• Clear consonants without mushiness
• Distinct vowels without distortion
• No clipping or audio artifacts on loud sounds
• Natural handling of sibilants (s, sh, ch)

Known problem areas

Test specifically for:

• Your brand name and product names
• Technical terms in your domain
• Common names that might be mispronounced
• Abbreviations and initialisms

Pace and rhythm

Speaking rate

• Appropriate speed for content type (narration vs. alerts vs. conversation)
• Consistent rate without unexpected speedups or slowdowns
• Natural variation—not metronomic but not erratic

Pausing

• Appropriate pauses at sentence boundaries
• Natural pauses at commas and clause boundaries
• No awkward pauses mid-phrase
• Pauses scale appropriately with punctuation (period > comma)

Rhythm patterns

• Natural stress patterns within sentences
• Appropriate emphasis on content words vs. function words
• Flow that doesn't feel choppy or run-on

Prosody—the stress, rhythm, and intonation of speech—is what separates natural-sounding voices from robotic ones. We break down [why prosody matters](/blog/posts/prosody-in-ai-voices-stress-rhythm-and-why-it-matters) in a dedicated guide.

Breath and naturalness

Breathing sounds

• Breathing sounds present but not intrusive (for natural voices)
• No unnatural gasps or breath artifacts
• Consistent breath pattern across utterances
• (Or) Clean breath removal if that's the goal

Naturalness markers

• Subtle pitch variation within sentences
• No robotic monotone
• No uncanny-valley expressiveness—see our piece on emotional AI voices for what can go wrong
• Sounds like someone you'd actually listen to

Audio quality

• No background noise or hum
• Consistent volume throughout
• Clean start and end (no clicks or artifacts)
• Appropriate audio levels for playback context

Consistency

Within a piece

• Same voice character throughout
• Consistent energy level
• No jarring quality shifts between sentences
• Stable pronunciation of repeated words

Across sessions

• Same voice on regeneration
• Consistent pronunciation choices
• Reproducible quality level
• Stable model behavior over time

At different lengths

• Quality holds for long-form content
• No degradation in multi-minute audio
• Natural paragraph and section transitions

Context-specific checks

For audiobooks/long-form

• Sustained naturalness over extended passages
• Appropriate handling of dialogue vs. narration
• Character distinction if multiple voices
• Chapter/section transitions

For voice assistants/short-form

• Clear and immediate comprehension
• Appropriate energy for notification/response use
• Natural question intonation
• Confirmations sound confident

For accessibility

• Comprehensible at various playback speeds
• Clear enough for users with hearing differences
• Works well with hearing aids/cochlear implants (test if possible)
• Not fatiguing for extended listening

See our guide on designing voice features for accessibility for more on this.

Running the evaluation

Create a test script

Include:

• Typical content you'll generate
• Edge cases (numbers, names, technical terms)
• Different content types (questions, statements, lists)
• Longer passages if relevant

Rate systematically

For each dimension, rate 1-5:

1. Unacceptable
2. Poor
3. Acceptable
4. Good
5. Excellent

Compare across providers

Use the same test script. Listen blind if possible (don't know which provider produced which sample). Our TTS evaluation guide for product teams covers the full comparison process.

Get multiple listeners

Your ear adapts. Fresh listeners catch issues you've normalized.

Red flags

• Pronunciation errors on common words
• Unnatural pauses that break comprehension
• Quality that degrades over longer passages
• Inconsistency between regenerations
• Artifacts or noise in the audio

For a broader overview of what to look for when choosing a provider, see our TTS API comparison.