Announcing our Series A Funding

Announcing our Series A Funding

AI Voices for Accessibility & Screen Readers

Experienced screen reader users often run well above normal speed. Speed adjusts from 0.5 to 2.0, and nine Indic languages are covered, where assistive audio is thin across the whole industry.

VOICES FOR THIS USE CASE

Andreiandrei
MaleYoungRussian
Best for RussianUse voice
Finnfinn
MaleYoungGerman
Best for GermanUse voice
Charancharan
MaleYoungIndian
Best for HindiUse voice
Solveigsolveig
FemaleYoungNorwegian
Best for NorwegianUse voice
Nikolainikolai
MaleYoungRussian
Best for RussianUse voice
Aryanaryan
MaleYoungIndian
Best for HindiUse voice

Pace belongs to the listener

Experienced screen reader users often prefer speeds well above 1.0, faster than most sighted designers would find comfortable. Speed is adjustable from 0.5 to 2.0, and the right decision is to expose that control rather than to choose a default on the user's behalf.

Predictability over personality

Assistive audio is heard constantly, so an expressive voice becomes tiring in a way it would not in narration. Consistency across renders matters more than character. There is also no emotion parameter, which in this context is closer to a feature than a limitation.

Indic language coverage

Nine Indic languages carry recommended voices. Screen reader support in Tamil, Telugu, Kannada, Malayalam, Marathi, Gujarati, Punjabi, Odia and Bengali is thin across the industry, which makes this the part of the catalog with the clearest accessibility argument behind it.

Building conformance yourself

There is no packaged accessibility integration. What exists is an API with speed control, twelve languages and streaming transports. Conformance work, including how audio is announced and interrupted, stays in your application. The voice layer supports that work rather than delivering it.

SPECIFICATION

Sample rate

44.1 kHz native, resampled to 8 kHz for telephony

Latency

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

Output formats

ulaw, alaw, PCM 16-bit, WAV, MP3

Streaming transports

WebSocket, HTTP chunked transfer

Speed range

0.5× – 2.0×, set per request

Explore Voice Similar to

AI Voices for Accessibility & Screen Readers

AI Voices for E-Learning

Course audio has to sound the same in module ten as in module one. These voices hold consistent across sessions, handle technical vocabulary through pronunciation dictionaries, and cover nine Indic languages alongside English.

AI Voices for Announcements & Public Transport

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A novel is thousands of requests stitched together, and the joins are where narration falls apart. These voices hold consistent across a full book and cover nine Indic languages that most vendors do not.

Calm Voices

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Conversational Voices

One hundred and ninety five voices carry the conversational tag, 84 percent of the catalog. Streaming is what makes them feel live: audio begins playing before the sentence has finished rendering.

Multilingual & Code-Switching AI Voices

Seventy two voices cover eleven Indic languages plus English, so one voice serves a Hindi caller and a Tamil caller without re-casting. Hindi and English can alternate inside a single sentence.

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AI Voices for IVR & Telephony

IVR voices need to survive a compressed phone line, not just sound good in a browser. These output ulaw and alaw directly, generate in roughly 200 milliseconds, and handle Hindi and English in the same prompt.

AI Voices for Meditation & Wellness

Guided audio lives on pacing rather than voice character. Speed goes down to 0.5, pauses come from how you write the script, and nine Indic languages are available for regional wellness content.

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Support calls reach people who are already inconvenienced, often on a poor line. These voices favour clarity over character, hold steady across renders, and move between Hindi and English the way callers actually do.

AI Voices for E-Learning

Course audio has to sound the same in module ten as in module one. These voices hold consistent across sessions, handle technical vocabulary through pronunciation dictionaries, and cover nine Indic languages alongside English.

AI Voices for Announcements & Public Transport

Announcements are heard in reverberant halls and repeated thousands of times. Pronunciation dictionaries fix station and place names permanently, ulaw and alaw feed public address hardware directly, and one voice carries a full multilingual chain.

AI Voices for Audiobook Narration

A novel is thousands of requests stitched together, and the joins are where narration falls apart. These voices hold consistent across a full book and cover nine Indic languages that most vendors do not.

Calm Voices

Calm comes from pace more than from voice. Speed runs down to 0.5, and pauses come from punctuation rather than a parameter, so the script does as much work as the casting.

Conversational Voices

One hundred and ninety five voices carry the conversational tag, 84 percent of the catalog. Streaming is what makes them feel live: audio begins playing before the sentence has finished rendering.

Multilingual & Code-Switching AI Voices

Seventy two voices cover eleven Indic languages plus English, so one voice serves a Hindi caller and a Tamil caller without re-casting. Hindi and English can alternate inside a single sentence.

AI Voices for Podcast Generation

Podcast production is segment based already, which suits generation in short calls. Intros and ad reads generate once and cache, and pairing contrasting voices produces a two-hander without booking two people.

AI Voices for IVR & Telephony

IVR voices need to survive a compressed phone line, not just sound good in a browser. These output ulaw and alaw directly, generate in roughly 200 milliseconds, and handle Hindi and English in the same prompt.

FAQs

Clarity is not a tagged attribute, so there is no filter for it. In practice, moderate pace matters more than voice choice: try 0.9 speed before switching voices. Andrea and Alec are steady in English, Gargi and Harshita in Hindi. All twelve supported languages are available for screen reader use.