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<header class="topnav">
<span class="site-title">Audio Understanding Experiment</span>
<nav>
<a href="index.html" class="active">Overview</a>
<a href="listen.html">Listen</a>
<a href="results.html">Results</a>
<a href="findings.html">Findings</a>
</nav>
<span class="doi"><a href="https://doi.org/10.57967/hf/8154">DOI: 10.57967/hf/8154</a></span>
</header>
<div class="audio-bar">
<span class="bar-label">Voice Sample</span>
<audio controls preload="none" src="voice-sample.flac"></audio>
<span class="bar-date">26 Mar 2026 · 20m 54s</span>
</div>
<div class="page-body">
<div class="hero">
<h1>Evaluating Audio Understanding in Multimodal AI</h1>
<p class="tagline">
A systematic experiment testing Gemini 3.1 Flash Lite's ability to analyse a 20-minute voice recording
across 137 structured prompts spanning speaker analysis, emotion detection, audio engineering,
forensic audio, demographics, and more.
</p>
<div class="meta-row">
<span class="meta-pill accent">49 completed evaluations</span>
<span class="meta-pill accent">13 categories tested</span>
<span class="meta-pill accent">137 total prompts</span>
<span class="meta-pill">Model: Gemini 3.1 Flash Lite</span>
<span class="meta-pill">Date: 26 March 2026</span>
<span class="meta-pill">Audio: FLAC mono 24kHz, 20m 54s</span>
</div>
</div>
<div class="section">
<h2>Objectives</h2>
<ul>
<li><strong>Breadth of capability:</strong> How many distinct audio analysis tasks can a multimodal model meaningfully perform from a single voice recording?</li>
<li><strong>Acoustic vs. content inference:</strong> Can the model distinguish between what it hears in the audio signal and what it understands from the speech content?</li>
<li><strong>Safety boundaries:</strong> Where does the model draw ethical lines on sensitive inferences (health, demographics, deception)?</li>
<li><strong>Practical utility:</strong> Are the outputs actionable for real-world use cases like audio production, voice cloning, and speech coaching?</li>
<li><strong>Internal consistency:</strong> Does the model maintain a coherent characterisation of the speaker across dozens of independent prompts?</li>
</ul>
</div>
<div class="section">
<h2>Methodology</h2>
<p>
A single freeform voice recording was made by Daniel Rosehill on a OnePlus Nord 3.5G phone in HQ mode (WAV 44.1kHz,
converted to FLAC mono 24kHz). The recording is unscripted, covering topics from voice cloning and TTS technology to
personal background and current events. The speaker was fatigued from disrupted sleep, providing a natural test of
the model's ability to detect vocal state.
</p>
<p>
137 test prompts were designed across 22 categories. 49 prompts were implemented with full prompt text and executed
against the audio using Gemini 3.1 Flash Lite via the Google Generative AI API. Each prompt was run independently
with the full audio file as context. The remaining 88 prompts are catalogued as suggested extensions.
</p>
</div>
<div class="section">
<h2>The Voice Sample</h2>
<p>
The recording features a male speaker in his late 30s with an Irish accent (Cork origin), living in Jerusalem for ~11 years.
Voice type: bass/low baritone (median F0 ~110 Hz). Speaking rate: ~169 WPM. Recorded in an untreated room while pacing.
A timestamped transcript (97.4% confidence, AssemblyAI) and detailed acoustic analysis (pitch, formants, signal levels)
are included in the dataset.
</p>
</div>
<div class="section">
<h2>Explore</h2>
<div class="card-grid">
<a href="listen.html" class="nav-card">
<div class="card-num">20:54</div>
<h3>Listen to the Audio</h3>
<p>Full waveform player with transcript and acoustic profile.</p>
</a>
<a href="results.html" class="nav-card">
<div class="card-num">49</div>
<h3>Browse Results</h3>
<p>All prompt-output pairs organised by category with the original prompts.</p>
</a>
<a href="findings.html" class="nav-card">
<div class="card-num">10</div>
<h3>Key Findings</h3>
<p>Cross-cutting analysis of model capabilities, limitations, and safety boundaries.</p>
</a>
<a href="https://huggingface.co/datasets/danielrosehill/Audio-Understanding-Test-Set" class="nav-card" target="_blank">
<div class="card-num" style="font-size:1.2rem;">HF</div>
<h3>Download Dataset</h3>
<p>JSONL prompts, results, audio files, transcript, and acoustic analysis on Hugging Face.</p>
</a>
</div>
</div>
<div class="section">
<h2>Cite This Work</h2>
<div class="cite-box">
Rosehill, D. (2026). <em>Audio Understanding Test Set</em>. Hugging Face.
<a href="https://doi.org/10.57967/hf/8154">https://doi.org/10.57967/hf/8154</a>
</div>
<p style="margin-top:0.75rem;">
Dataset: <a href="https://huggingface.co/datasets/danielrosehill/Audio-Understanding-Test-Set">danielrosehill/Audio-Understanding-Test-Set</a>
· Source: <a href="https://github.com/danielrosehill/Audio-Understanding-Test-Prompts">GitHub</a>
· DOI: <a href="https://doi.org/10.57967/hf/8154">10.57967/hf/8154</a>
</p>
</div>
<div class="footer">
Created by Daniel Rosehill with assistance from Claude (Opus 4.6). Licensed under CC-BY-4.0.
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