Strip filler words from voice dictation before prompt submission

🤖 *AI response on behalf of Casey*

### Feature summary

Add an optional post-processing step for GitHub Copilot app voice dictation that removes filler/dead words such as "um", "uh", "ah", "you know", and repeated disfluencies before the prompt is submitted to the model.

### What problem are you trying to solve?

Voice dictation often captures natural speech artifacts that are useful while thinking aloud but not useful in the final prompt. These filler words can make prompts noisier, reduce clarity, and consume extra tokens over time.

Example rough dictation:

> Um, can you uh update the tests, you know, for the new parser behavior?

Cleaner prompt:

> Can you update the tests for the new parser behavior?

### Proposed solution

Before sending dictated text to the model, run lightweight sanitization that can:

- Remove common filler words and repeated verbal pauses.
- Collapse obvious repeated fragments caused by speech-to-text corrections.
- Preserve intentional technical terms and quoted/code text.
- Be configurable or disableable if users prefer exact transcription.

This could be local deterministic cleanup, model-assisted polishing, or part of the existing voice dictation pipeline.

### Workflow impact

This would make voice input more efficient for users who dictate longer prompts, especially in agentic workflows where prompt clarity matters. It should also reduce small amounts of token usage per voice prompt, which could add up across frequent dictation users.

### Related context

Related VS Code backlog request: https://github.com/microsoft/vscode/issues/315508

That request tracks similar speech-input post-processing for VS Code, including removing filler words like "um" and "ah" and reducing invalid/low-value tokens before Copilot processes the input.

The issue names no repository files, tests, or entry points, so first locate the voice-dictation path and prompt-submission boundary in the app. Define the sanitization scope, configurability, and preservation rules, then add coverage showing filler removal, repeated-fragment handling, and unchanged technical or quoted text before submission.

Written by the indexing model from the issue text.