Computer Vision Israel Meetup

Aug 06, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions

Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.
In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance.

About the Speaker

Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication. Her research focuses on instruction-following speech models with speech as both input and output, with a recent emphasis on full-duplex systems. Beyond her research, she co-organises the instruction-following and speech translation metrics shared tasks at IWSLT. She is a 2026 Apple Scholar in AI/ML.

AI based Audio Forensics

In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.

The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.

About the Speaker

Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.

Curating, Searching, and Evaluating Audio Datasets in FiftyOne

In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.

We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.

Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.

About the Speaker

John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.

Real-Time ASR at 4x on Consumer Hardware: The Meetily Architecture

This talk covers the engineering behind Meetily, an open-source meeting assistant that runs Whisper and NVIDIA Parakeet transcription entirely on-device. We'll walk through how we got Parakeet to roughly 4x real-time on consumer hardware, and the specific points where it still falls over.

We'll also get into the honest trade-offs between local and cloud inference: latency, accuracy, cost, and what you actually give up by choosing one over the other. Wrapping ML inference in a Rust/Tauri desktop app came with its own costs, which we'll unpack as well.

Finally, we'll look at what "fully local" really means at an architecture level, where that boundary sits, and how easily it leaks once you add model downloads, integrations, or a pluggable LLM backend.

About the Speaker

Sandeep Zachariah is the Founder and CEO of Zackriya Solutions and the leads the team behind Meetily, an open-source, privacy-first meeting assistant that runs Whisper and NVIDIA Parakeet transcription entirely on-device. He brings a rare full-stack perspective on audio AI — from low-level embedded systems and hardware acceleration up through real-time ASR and local LLM summarization — with deep experience deploying speech and ML models across servers, GPUs and consumer hardware.

Aug 06, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom

Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions

Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.
In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance.

About the Speaker

Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication.

AI based Audio Forensics

In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.

The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.

About the Speaker

Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.

Curating, Searching, and Evaluating Audio Datasets in FiftyOne

In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.

We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.

Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.

About the Speaker

John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.

Aug 11, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

As robotics and autonomous vehicle teams move from traditional perception models to end-to-end Physical AI systems, understanding model behavior is becoming harder than ever. These models ingest synchronized inputs from cameras, sensors, and other data streams, but their decisions can be difficult to explain, reproduce, and improve.

You’ll learn how to use multimodal data to investigate questions like: when did the model swerve, miss an object, misinterpret a scene, or behave unexpectedly — and how can you find every similar moment across your dataset?

Designed for robotics, AV, and machine learning teams, this session will show how FiftyOne helps turn multimodal data into a scalable workflow for model evaluation, debugging, and improvement.

Aug 13, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.

By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.

Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow, Docker, and OpenCV. I started as a software developer, moved into AI, led teams, and served as CTO. Today, I connect code and community to build open, production-ready AI, making technology simple, accessible, and reliable.