VoiceVisionReasoner

Multimodal Reasoning and Intent-Preserving Safety for Voice and Vision

VoiceVisionReasoner is a research prototype that processes speech + visual context together. It produces grounded responses, avoids punitive refusals, and respects user intent.

This project explores multimodal failures of current assistants and proposes a simple approach:

Whisper ASR (speech → text)
Vision captioning (image → description)
Joint reasoning (text + vision → answer)
Intent-preserving safety checks

1. Motivation

Most AI assistants treat modalities as independent:

Speech → transcription → answer
Image → caption → answer

This siloed pipeline often produces:

generic replies
moralizing responses
refusals framed as safety
advice that ignores emotional context
hallucinations unrelated to user environment

Most failures are not user attacks. They arise because the system fails to interpret human signals.

VoiceVisionReasoner attempts to reduce these failures by combining:

Speech transcription
Visual description
Joint reasoning
Safety and tone checks

2. System Overview

2.1 Processing Pipeline

Audio (wav) → Whisper ASR → Transcript Image (jpg/png) → Caption Model → Caption Transcript + Caption → LLM → Reasoned Answer ↓ Tone / Safety Analysis

2.2 Components

Speech (ASR)

Whisper generates transcription from spoken audio.

Vision (Captioning)

A captioning model describes the visible environment.

Reasoning

A language model produces answers grounded in transcript + caption.

Safety

Lightweight checks detect:

toxic tone
hallucinations
punitive refusals
moralizing language

The system repairs intent only when needed.

3. Core Idea

Typical LLMs treat:

emotion as danger
uncertainty as hallucination
user vulnerability as policy violation

VoiceVisionReasoner takes a different stance:

Safety = collaboration, not punishment. Use context → propose constructive actions → preserve user intent.

4. Quick Start

4.1 Installation

git clone https://github.com/kritibehl/VoiceVisionReasoner.git

cd VoiceVisionReasoner pip install -r requirements.txt

4.2 Example Run

python app.py --audio_path examples/query.wav --image_path examples/desk.png

The program outputs:

speech transcript
image caption
joint reasoning answer
safety/tone indicators

5. Example Interaction

User (spoken): “Why does my workspace make me feel stressed?”

Image: A cluttered desk with cables, notebooks, and unopened mail.

System output: Clutter increases decision fatigue. Keep items you use daily within reach. Move infrequent objects into a drawer or box. Take a 5-minute break afterward.

The answer is grounded, actionable, and non-judgmental.

6. Relationship to Prior Work

FairEval-Suite

Human-aligned evaluation for LLM responses. Assesses clarity, relevance, tone, hallucination risk. https://github.com/kritibehl/FairEval-Suite

JailBreakDefense

Intent-preserving prompt repair. Converts adversarial or distressed prompts into constructive goals. https://github.com/kritibehl/JailBreakDefense