AI voice in 2026: compare speech synthesis, transcription and audio agents

Separate three audio tasks

Text-to-speech (TTS) converts text into sound. Narration needs intelligibility, pronunciation, rhythm and voice consistency. An expressive performance may suit a story but distract from a procedure.

Speech-to-text (STT) converts sound into text. Evaluate word errors and critical changes to names, amounts and negations. Speaker attribution and timestamps may matter more than elegant punctuation.

Audio conversation adds turn-taking, interruption and sometimes tools. Fast output is not enough if the agent talks over corrections or confirms an unrequested action.

Currently documented families

Sources: OpenAI models, Google TTS, Google Live, ElevenLabs, Voxtral TTS, Mistral models and Grok release notes.

Names and availability change. Existing integrations should also check OpenAI deprecations. A new family is not proof of better performance on your accent, terminology or recordings.

Test narration with original scripts

Prepare five scripts of 80–120 words: neutral instructions; dates and amounts; acronyms and fictional names; mixed-language terminology; and an explanation with a deliberate pause before the answer.

Give every candidate the same text. Record voice, model, settings, audio format and date. Ask listeners to identify mispronounced words before expressing an overall preference, with the source hidden if possible.

Score intelligibility, pronunciation, rhythm, consistency and editing effort from 0 to 2. Define a blocking error, such as reading fifteen instead of fifty. Check audio on both a phone speaker and headphones because listening conditions affect usability.

Evaluate transcription against a reference

Use an authorised recording and a manually reviewed reference transcript. Define conventions for punctuation, hesitations and numbers. Otherwise some apparent model differences are differences in grading.

Word error rate, WER, is substitutions plus deletions plus insertions divided by reference word count. In a fictional example, four substitutions, three deletions and one insertion across 100 reference words give 8% WER. This does not distinguish a harmless wording error from a reversed negation.

Keep a separate critical-error list covering names, amounts, deadlines and commitments relevant to your task. For meetings, verify who said each statement. A summary must not silently turn a proposal into a decision.

Measure the full conversational delay

Publisher inference latency is not necessarily the delay a person experiences. Network transport, end-of-speech detection, tools and audio playback add time. Measure from the user's last spoken word to the first useful sound received across several exchanges.

Test an interruption, a correction and an ambiguous request. The agent should ask for clarification and retain context after interruption. If actions are possible, test confirmation using a simulation before connecting a real service.

Turn an article into useful audio

A complete reading is not always the best format. Start with a two-minute explanation, one example and a link to the activity. Adapt tables and links for listening: “see the second column” is unhelpful without the screen.

Keep accessible text, sources and corrections in the article. Combine narration with a controlled storyboard for video without personal filming. Use an authorised voice and verify the service's commercial-use terms; downloadable weights alone do not establish those rights.

Help me design an audio evaluation. First separate narration, transcription and conversation. For my task, propose five original cases, a verifiable reference, scoring criteria and critical errors. Include model, voice, version, settings, format, cost, correction time and permission for recordings. Do not claim to have listened to or tested outputs.

Download the audio evaluation worksheet. Our benchmark guide explains how to interpret external rankings.

Frequently asked questions

Does an advertised 100 ms latency guarantee fluent conversation? No. Establish what the number measures, then test the complete flow in your environment.

Is WER sufficient to select transcription? No. Examine meaning-changing errors and required workflow features as well.