# Conversational voice-agent localization > End-to-end localization of spoken agent experiences, from recognition and dialogue policy to synthesized voice and repair. [Speech & voice AI capability](https://admas.net/capabilities/speech/index.md) ## The challenge Voice agents combine ASR, turn-taking, dialogue state, tools, and TTS. A local-sounding prompt cannot compensate for name errors, latency, interruption failures, culturally wrong confirmation, or unsafe actions. We localize the conversation as a stateful spoken interaction, including how users start, interrupt, clarify, confirm, recover, and switch languages. Work connects native-speaker design with measurable ASR, dialogue, tool, latency, and TTS behavior. ## How Admas works 1. **Map spoken journeys:** Define intents, entities, language negotiation, turn states, confirmations, repair, escalation, privacy, and high-consequence actions. 2. **Adapt the stack:** Localize prompts and policies; tune lexicons, grammars, pronunciations, routing, voices, and tool parameters. 3. **Test real conversations:** Run task-based native-speaker sessions across accents, noise, interruptions, code-switching, latency, and failure states. ## Typical outputs - Localized voice interaction specification - Intent, entity, lexicon, and pronunciation assets - Conversation and tool-trajectory test suite - Native-speaker findings and launch gates ## Frequently asked questions ### What is conversational voice-agent localization? End-to-end localization of spoken agent experiences, from recognition and dialogue policy to synthesized voice and repair. In practice, the work is bounded by a defined product or model decision, named audiences and locales, representative inputs, and acceptance criteria that can be reviewed. ### When does a team need conversational voice-agent localization? Voice agents combine ASR, turn-taking, dialogue state, tools, and TTS. A local-sounding prompt cannot compensate for name errors, latency, interruption failures, culturally wrong confirmation, or unsafe actions. The useful starting point is the smallest representative flow that can expose the cause, impact, and ownership of the problem. ### What does a conversational voice-agent localization engagement include? Map spoken journeys: Define intents, entities, language negotiation, turn states, confirmations, repair, escalation, privacy, and high-consequence actions. Adapt the stack: Localize prompts and policies; tune lexicons, grammars, pronunciations, routing, voices, and tool parameters. Test real conversations: Run task-based native-speaker sessions across accents, noise, interruptions, code-switching, latency, and failure states. ### What should we provide before conversational voice-agent localization starts? The most useful inputs are target languages, dialects, and speaking contexts, audio or model access, speaker and consent requirements, acoustic conditions and devices, product tasks, scripts, prompts, and and quality thresholds. Admas can begin with a partial package, but missing context, rights, access, owners, or acceptance criteria will be made visible in the plan rather than treated as harmless assumptions. ### What does Admas deliver for conversational voice-agent localization? Typical outputs include localized voice interaction specification, intent, entity, lexicon, and pronunciation assets, conversation and tool-trajectory test suite, and native-speaker findings and launch gates. Deliverables are adapted to the team that must use them, with decisions, evidence, limitations, owners, and next actions made explicit. ### How is the quality of conversational voice-agent localization evaluated? Quality is measured against the real task and risk. Relevant evidence can include intelligibility and naturalness, task and recognition accuracy by cohort, pronunciation and prosody, speaker and acoustic coverage, latency, and accessibility and failure recovery. Sampling, severity rules, reviewers, adjudication, and pass or fail thresholds should be agreed before the result is used as a release decision. ### Can AI replace the human work in conversational voice-agent localization? Speech models can draft transcripts, synthesize candidates, segment audio, and surface likely errors. Native listeners, phoneticians, voice specialists, conversation designers, and engineers are still needed to judge pronunciation, prosody, intelligibility, demographic coverage, and real interaction failures. The right allocation depends on consequence, content stability, available references, language coverage, reversibility, and the cost of a plausible but wrong result. ### How much does conversational voice-agent localization cost? The estimate changes with recording or evaluation hours, languages, dialects, and speaker profiles, studio and equipment needs, transcription and annotation depth, model or integration work, and quality and consent controls. Pricing should distinguish setup and discovery, repeatable units, specialist or engineering time, independent review, management, and external costs. A low unit price is not comparable if it excludes the QA cycle or shifts rework back to the buyer. ## Related speech & voice ai services - [Speech data programs](https://admas.net/capabilities/speech/speech-data/index.md): Speaker, prompt, recording, transcription, and validation systems for multilingual speech development. - [Text-to-speech development](https://admas.net/capabilities/speech/text-to-speech/index.md): Language design, pronunciation resources, voice evaluation, and product QA for natural synthesized speech. - [Speech recognition evaluation](https://admas.net/capabilities/speech/speech-recognition/index.md): Language-aware ASR testing, error analysis, and improvement plans across speakers, accents, domains, and environments. - [Speech quality evaluation](https://admas.net/capabilities/speech/speech-quality-evaluation/index.md): Perceptual and task-based evaluation for recognition, synthesis, translation, enhancement, and spoken dialogue systems. ## Start a project - [Build a project brief](https://admas.net/start-a-project/index.md?focus=speech): Tell Admas what you are building, which modalities and languages matter, and where progress is blocked.