Guard your privacy with Spiki’s speech command recognition  

Guard your privacy with Spiki’s speech command recognition  

Where does your voice assistant keep your data?

Voice assistants make our lives so much easier: simply activating our lights, dropping a phone call or playing our favourite music by just using speech commands, hands free.  Several devices, assistants and routers provide us with formidable convenience at our spoken request. Of course, an active Internet connection is required for that. Sometimes different web apps come into play to further finetune and navigate tasks. It’s all quite simple and intuitive. Nothing to worry about.  


Do you value, beside the mere convenience of using voice assistants, your data privacy and security? Where does your voice assistant keep your data? Does it listen to you ALL THE TIME, storing, processing or even sharing the most personal details?

Risks of online voice processing devices 

Indeed, every user shares a great deal of information, even when only talking privately at home. Usually, these recorded data are stored and processed in a cloud. This poses a risk to security, safety and privacy. 

  • Firstly, hackers and cybercriminals can gain access to data stored in a cloud system. Sensitive information could get into the wrong hands, e.g. access codes, passwords, financial or health information etc. 
  • Secondly, a user’s voice is regarded as biometrical data which can identify a human being. Voice recordings stored locally are not in danger, but stored in a cloud they could get subject to abuse. Big tech corporations frequently store data generated from voice recognition devices in a cloud, analyse and use them in order to improve their software or their advertising.  
    • Here is a prime example: “ Inc. must produce millions of documents in response to discovery requests in a potential class action over the marketing of its Alexa-enabled devices and their recording of users’ conversations, a federal judge ruled”, reports Christopher Brown on Bloomberg Law (see article). 
    • Improper or not – some big tech companies already offer their users to opt in and allow their voice recordings to be stored, while others require them to actively opt out if they do not want this. Feel free to check for yourself the terms of use of your trusted voice assistant to stay in control. 
  • Any company’s information is a top security priority. In the wake of the COVID pandemic, many corporate meetings had to be transferred from personal to the online world. Video conference tools mainly use end-to-end encryption, which secures privacy and security of the online communication. Still, the encrypted video and audio content is stored in the cloud…you see where this is going, check first bullet point. 

In summary, data privacy and cyber security are major challenges for both private and corporate users of voice processing software and devices using speech commands.

How to guard your privacy and security 

There is only one reliable solution in order to protect your sensitive information: to store and process voice data locally or at the edge, which means closer to the device used. Beside keeping data secure, this would also solve latency issues slowing down the processing of data stored in a remote cloud server. 

Spiki’s voice command recognition keeps your data locally and guards your privacy.  

Relying on Spiki’s robust voice command recognition, you eliminate privacy and compliance risks and keep stored data safe. Reducing latency will reduce computation time. Since our IP can run on any third-party hardware, you save time and implementation costs. Spiki’s voice recognition is offline, to keep your private details private. 

  • It works without Internet connectivity  
  • It ensures privacy of customer data  
  • It is usable in noisy environments, robust and reliable to fulfil your specific requirements 
  • It requires no additional hardware (e.g., Alexa). 

Excited? Get in touch for receiving a demonstration! 


What is…robust AI? 

What is…robust AI? 

Reliable performance is the key

Robustness in AI can be described as predictive certainty of machine learning systems. Robust machine learning systems perform just as they have been trained to, even in unfamiliar settings, and minimise vulnerability to adversarial attacks. Put in other words: a robust AI can detect if input data is meaningfully different to what it has been trained on and mitigate unintended effects. Robustness is therefore a key prerequisite for deploying AI in safety-critical settings.1 The European Commission has, to mitigate possible negative effects of AI on society, established a set of principles for secure and trustworthy AI. Core requirements such as the concepts of explainability of AI systems and the aforementioned robusntess will also feature in future regulations of such technologies alongside the cybersecurity of digital systems and the protection of data.

When is a machine learning system robust enough for the real world? 

Imagine a system for image classification that has to determine whether the pictures or bits of pictures show cats or dogs. If slightly altered pixels, shaded spots or distorted angles of the input picture lead to a completely wrong classification, this modification is called an adversarial example. If AI models make mistakes they should not, this is the exact opposite of robustness. While funny with the cats and dogs example, this cannot stand in the real world, where, for example, an autonomously driving vehicle needs to clearly distinguish street signs and obstacles. 

Robustness implies that even with perturbed inputs, i.e. with any possible alteration or minuscule change to the unperturbed input, the model still classifies it correctly and does its job just like the human brain would. In practise, verification frameworks are used to test robustness in real world situtions compared to training.

The goal is clear: safety and reliability.  

Robustness solves the “never enough data” problem 

Whenever an AI is trained for robustness the question arises: how much learning input, i.e. data, is enough to guarantee its functioning? The answer usually is – there is never enough, the number of datapoints or inputs is infinite. That is what makes training an AI costly and time-intensive. Robustness in training makes sure that the input data points are clearly defined and specified. This reduces the amount of input data to a finite number and saves both time and money. 

Spiki makes AI robust and reliable 

SPIKI is building brain-inspired AI you can trust. We developed an innovative neural networks training method for supervised learning that combines 

  • Robust Neural Networks  
  • Specification based training data for reduced data collection efforts 
  • Built-in formal verification for explainable AI. 

We are striving to build trustworthy AI, which can be deployed via microcontroller, FPGA, as a cloud service or even ASIC as a future step in our product development. You profit from an easy to use toolchain, from training to deployment, ready for third-party hardware. 

Excited? Contact us! 



1 Tim G. J. Rudner and Helen Toner, Key Concepts in AI Safety: Robustness and Adversarial Examples, Center for Security and Emerging Technology, March 2021.

2 Hamon, R., Junklewitz, H., Sanchez, I. Robustness and Explainability of Artificial Intelligence – From technical to policy solutions, Publications Office of the European Union, Luxembourg, Luxembourg, 2020