Sultry AI Voices

Sultry AI Voices

There is no tone field in the catalog, so these six were chosen by listening rather than returned by a filter. What they share is an unhurried delivery and a lower placement, which is most of what the word describes.

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Sultry AI Voices
AmeliaVerified
American · Female · Young
United States flagEnglish
Use voice
KennedyVerified
American · Female · Young
United States flagEnglish
Use voice
SavannahVerified
American · Female · Young
United States flagEnglish
Use voice
CormacVerified
British · Male · Young
United Kingdom flagEnglish
Use voice
MalarVerified
Indian · Female · Young
India flagHindi
Use voice
GopalVerified
Indian · Male · Young
India flagHindi
Use voice
AmeliaVerified
American · Female · Young
United States flagEnglish
Use voice
KennedyVerified
American · Female · Young
United States flagEnglish
Use voice
SavannahVerified
American · Female · Young
United States flagEnglish
Use voice
CormacVerified
British · Male · Young
United Kingdom flagEnglish
Use voice
MalarVerified
Indian · Female · Young
India flagHindi
Use voice
GopalVerified
Indian · Male · Young
India flagHindi
Use voice

German AI Voices

Female Voices

Warm Voices

AI Voices for Podcast Generation

British English AI Voices

AI Voices for Documentary Voice Over

Calm Voices

Australian English AI Voices

AI Voices for Gaming & Character Voices

Professional Voices

AI Narrator Voices

Cheerful Voices

AI Voices for Customer Support Automation

Authoritative Voices

AI Voices for Meditation & Wellness

Gravelly AI Voices

AI Voices for YouTube Voiceover

Indian English AI Voices

AI Voices for Movie Trailers

Friendly Voices

Narration is the one job where a voice has to hold up for hours rather than seconds. 163 of the 244 voices here carry the narration tag, the largest group in the catalog, across English, Hindi and nine other Indic languages.

1500+ voices to explore

Browse our full voice library and find the right tone, accent, and personality for your agent.

Model Specifications

Streaming transports

WebSocket, HTTP chunked transfer

WebSocket, HTTP chunked transfer

Output formats

.ulaw, .alaw, PCM 16-bit, .wav, .mp3

.ulaw, .alaw, PCM 16-bit, .wav, .mp3

Speed range

0.5× – 2.0×, set per request

0.5× – 2.0×, set per request

Sample rate

44.1 kHz native, resampled to 8 kHz for telephony

44.1 kHz native, resampled to 8 kHz for telephony

Latency

~200 ms to first byte (p50, warm region)

~200 ms to first byte (p50, warm region)

Why no filter returns this

The catalog records gender, age, accent, language and use case. It records nothing about timbre or tone, so no query returns sultry voices and this set reflects listening rather than data. Treat it as a shortlist to audition. Any page claiming a filtered result for this is describing something that does not exist.

Where this register is actually used

Trailers, fragrance and spirits advertising, hospitality and late-night radio imaging. All short-form, all script-led, and all better served by writing short sentences with space around them than by hunting for a particular voice.

Pace does more than casting

Speed runs from 0.5 to 2.0, and a setting around 0.85 accounts for most of what people mean by the word. That is the only delivery control available, since there is no pitch or emotion parameter. Slowing a read changes its character more reliably than swapping voices does.

What cannot be adjusted

There is no pitch control, so a voice cannot be lowered after generation. Placement is a property of the voice you cast. If a specific person's delivery is the requirement, cloning reproduces it directly, with that person's consent.

Popular voices

Frequently asked questions

These six, chosen by ear from the narration pool. The catalog has no tone or timbre field, so this is a judgment rather than a filtered list. Listen to all six before deciding, since nothing documents the differences between them.