Jordan-Hall / Jordan-Hall/browser

[P1][VOICE-02] Semantic commands and dictation

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Description

Programme: #1
Epic: #22

## Objective
Turn speech into reliable contextual interaction by separating dictation from commands and resolving references against explicit semantic foreground context.

## Scope
- Distinct command activation and dictation modes.
- Resolve “this/these/current/that one” against WS-04 selected object/panel/evidence/file IDs.
- Command parsing into GoalContract edits, deterministic UI actions or bounded task requests.
- User-managed technical vocabulary and corrections for names, repositories, identifiers and product terms.
- Confirmation/revision before ambiguous semantic targeting.
- Nearby/quoted speech handling that remains content rather than executable command.
- History showing transcript → interpreted command → resolved target.

## Safety rules
- Voice matching is not authorization.
- Purchases, bids, sharing, deletion, messages and security changes still require the normal trusted approval policy.
- No unrestricted continuous desktop observation just to resolve pronouns.

## Acceptance criteria
- [ ] Dictated text is never executed merely because it contains imperative language.
- [ ] Commands resolve to explicit selected/context IDs and fail when ambiguity is material.
- [ ] Quoted/article/nearby speech cannot authorize side effects.
- [ ] Corrections update the intended target/text without restarting the whole task.
- [ ] Target/account/recipient changes invalidate stale command interpretation where needed.
- [ ] Command interpretation and resolved context are inspectable in task history.

## Dependencies
- VOICE-01
- WS-04
- DATA-04

**First phase:** P1
**Maturity target:** P3
**Owner:** local-ai-speech

Contributor guide

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Research direction

No files, tests, or entry points are named. Start by reading the VOICE-01, WS-04, and DATA-04 dependencies, then map the acceptance criteria for dictation, contextual target resolution, confirmation, corrections, safety boundaries, and inspectable task history before defining the implementation.

Written by the indexing model from the issue text.

Assessment

Domain
ai
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
30/100

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