Speech & voice AI

Speech data, TTS, and STT systems built for natural output and dependable recognition across languages. Admas treats it as a connected practice spanning Speech data programs, Text-to-speech development, Speech recognition evaluation, Conversational voice-agent localization, and Speech quality evaluation. A project can start with one focused service and expand only where the evidence shows a dependency.

This work is usually shared by speech, product, data, accessibility, conversation-design, and market teams building or localizing voice experiences. The exact team depends on who owns the affected user journey, data, system, content, market decision, and release risk.

Start before a launch is locked when possible. Common signals include a voice is intelligible but does not sound natural or local, recognition performance collapses across accents and environments, low-resource languages lack reliable speech data, and teams cannot explain why speech metrics and human judgment disagree. A focused diagnostic can still help when the work has already become a recovery project.

Useful starting 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. They do not need to be complete: unknowns should be recorded as assumptions, risks, or discovery questions rather than silently filled in.

The practice rarely stands alone. Product architecture affects localization; data affects model behavior; language quality affects release decisions; and program design affects whether improvements persist. Admas maps those handoffs explicitly so each specialist can work from the same acceptance criteria.

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.

Use evidence tied to the intended decision, not one universal score. Typical measures include intelligibility and naturalness, task and recognition accuracy by cohort, pronunciation and prosody, speaker and acoustic coverage, latency, and accessibility and failure recovery. Results should be segmented by language, market, content or task type, and risk so an average cannot hide a serious local failure.

Pricing depends on 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. A defensible estimate separates repeatable production units from discovery, engineering, review, management, pass-through costs, and contingency. Admas scopes the acceptance criteria and review path before treating a volume number as a quote.