The Hidden Audio Bias Inside Audio-Visual Speech Recognition
Shapley analysis reveals why AVSR models keep trusting corrupted audio, exposing a hidden bias in multimodal speech recognition. Read All
New Story
The Hidden Audio Bias Inside Audio-Visual Speech Recognition
byaimodels44byaimodels44@aimodels44
Among other things, launching AIModels.fyi ... Find the right AI model for your project - https://aimodels.fyi
SubscribeApril 5th, 2026
audio element.Speed1xVoiceDr. One Ms. Hacker byaimodels44@aimodels44byaimodels44@aimodels44Among other things, launching AIModels.fyi ... Find the right AI model for your project - https://aimodels.fyi
Subscribe
Among other things, launching AIModels.fyi ... Find the right AI model for your project - https://aimodels.fyi
Subscribe← Previous
Matrix-Game-3.0 Brings Real-Time 720p Interactive Video to Open Source
About Author
Among other things, launching AIModels.fyi ... Find the right AI model for your project - https://aimodels.fyi
Read my storiesAbout @aimodels44
Comments
TOPICS
machine-learning#artificial-intelligence#software-architecture#data-science#performance#design#audio-visual-speech-ai#avsr-interpretability#speech-recognition-bias
THIS ARTICLE WAS FEATURED IN
Related Stories
10 Languages, 9 Premium Voices: Meet Qwen3-TTS CustomVoice
aimodels44
Feb 11, 2026
The Noonification: Use This 7-Step McKinsey Framework to Solve Any Problem (1/10/2023)
Noonification
Jan 10, 2023
The Noonification: A Taxonomy of Inclusiveness (1/11/2024)
Noonification
Jan 11, 2024
The Noonification: What is the InfiniteNature-Zero AI Model? (11/19/2022)
Noonification
Nov 19, 2022
10 Ways AI Has Changed Our Lives
Bella Williams
Mar 04, 2020
100 Days of AI, Day 8: Experimenting With Microsoft's Semantic Kernel Using GPT-4
Nataraj
Jan 31, 2024
10 Languages, 9 Premium Voices: Meet Qwen3-TTS CustomVoice
aimodels44
Feb 11, 2026
The Noonification: Use This 7-Step McKinsey Framework to Solve Any Problem (1/10/2023)
Noonification
Jan 10, 2023
The Noonification: A Taxonomy of Inclusiveness (1/11/2024)
Noonification
Jan 11, 2024
The Noonification: What is the InfiniteNature-Zero AI Model? (11/19/2022)
Noonification
Nov 19, 2022
10 Ways AI Has Changed Our Lives
Bella Williams
Mar 04, 2020
100 Days of AI, Day 8: Experimenting With Microsoft's Semantic Kernel Using GPT-4
Nataraj
Jan 31, 2024
Hackernoon AI
https://hackernoon.com/the-hidden-audio-bias-inside-audio-visual-speech-recognition?source=rssSign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelanalysismultimodal
Paper close reading: "Why Language Models Hallucinate"
People often talk about paper reading as a skill, but there aren’t that many examples of people walking through how they do it. Part of this is a problem of supply: it’s expensive to document one’s thought process for any significant length of time, and there’s the additional cost of probably looking quite foolish when doing so. Part of this is simply a question of demand: far more people will read a short paragraph or tweet thread summarizing a paper and offering some pithy comments, than a thousand-word post of someone’s train of thought as they look through a paper. Thankfully, I’m willing to risk looking a bit foolish, and I’m pretty unresponsive to demand at this present moment, so I’ll try and write down my thought processes as I read through as much of a a paper I can in 1-2 hours.
OpenClaw Changed How We Use AI. KiloClaw Made It Effortless to Get Started
OpenClaw is a powerful open-source AI agent, but self-hosting it is a pain. KiloClaw is OpenClaw fully hosted and managed by Kilo — sign up, connect your chat apps, and your agent is running in about a minute. No Docker, no YAML, no server babysitting. People are using it for personalized morning briefs, inbox digests, auto-building CRMs, browser automation, GitHub triage, and more. Hosting is $8/month with a 7-day free trial, inference runs through Kilo Gateway at zero markup across 500+ models, and it's free for open-source maintainers. Read All
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
Paper close reading: "Why Language Models Hallucinate"
People often talk about paper reading as a skill, but there aren’t that many examples of people walking through how they do it. Part of this is a problem of supply: it’s expensive to document one’s thought process for any significant length of time, and there’s the additional cost of probably looking quite foolish when doing so. Part of this is simply a question of demand: far more people will read a short paragraph or tweet thread summarizing a paper and offering some pithy comments, than a thousand-word post of someone’s train of thought as they look through a paper. Thankfully, I’m willing to risk looking a bit foolish, and I’m pretty unresponsive to demand at this present moment, so I’ll try and write down my thought processes as I read through as much of a a paper I can in 1-2 hours.
Qwen3.5-4B GGUF quants comparison (KLD vs speed) - Lunar Lake
I wanted to know which type of quant is the best on this laptop (Intel 258V - iGPU 140V 18GB), so I tested all these small quants hoping that it generalizes to bigger models: Winners in bold (KLD≤0.01) Uploader Quant tk/s KLD GB KLD/GB* mradermacher* Q4_0 28.97 0.052659918 2.37 0.04593 mradermacher_i1 Q4_0 28.89 0.059171561 2.37 0.05162 mradermacher_i1 IQ3_XXS 28.59 0.177140713 1.77 0.20736 Unsloth UD-IQ2_XXS 28.47 0.573673327 1.42 0.83747 Unsloth Q4_0 28.3 0.053431218 2.41 0.04583 Bartowski Q4_0 28.28 0.049796789 2.45 0.04200 mradermacher Q4_K_S 27.74 0.050305722 2.39 0.04350 Unsloth Q4_K_S 27.29 0.028402815 2.41 0.02429 Unsloth UD-IQ3_XXS 27.03 0.146879419 1.82 0.16718 mradermacher Q2_K 26.98 0.858648176 1.78 1.00000 mradermacher_i1 Q4_K_M 25.95 0.026540567 2.52 0.02169 mradermacher_i1 I
Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
arXiv:2604.02821v1 Announce Type: new Abstract: This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth goal-conditioned neural ordinary differential equations (neural ODEs) via bi-Lipschitz diffeomorphisms. Theoretical results show that the proposed model can provide guarantees of global exponential stability and safety (safe set forward invariance) regardless of goal location. Moreover, explicit bounds on convergence rate, tracking error, and vector field magnitude are established. Our approach admits a tractable learning implementation using bi-Lipschitz neural networks and can incorporate demonstration data. We illustrate the effectiveness of the proposed method
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!