Be Careful What You Ask For: Voice Control

February 19, 2025 by Al Williams 52 Comments

We get it. We also watched Star Trek and thought how cool it would be to talk to our computer. From Kirk setting a self-destruct sequence, to Scotty talking into a mouse, or Picard ordering Earl Grey, we intuitively know that talking to a computer is better than typing, right? Well, computers talking back and forth to us is no longer science fiction, and maybe we aren’t as happy about it as we thought we’d be.

We weren’t able to pinpoint the first talking computer in fiction. Asimov and van Vogt had talking computers in the 1940s. “I, Robot” by Eando Binder, and not the more famous Asimov story, had a fully speaking robot in 1939. You could argue that “The Machine” in E. M. Forster’s “The Machine Stops” was probably speaking — the text is a little vague — and that was in 1909. The robot from Metropolis (1927) spoke after transforming, but you could argue that doesn’t count.

Meanwhile, In Real Life

In real life, computers weren’t as quick to speak. Before the middle of the twentieth century, machine-generated speech was an oddity. In 1779, a mechanical contrivance by Wolfgang von Kempelen, famous for the mechanical Turk chess-playing automaton, could form simple words. By 1939, Bell Labs could do even better speech synthesis electronically but with a human operator. It didn’t sound very good, as you can see in the video below, but it was certainly expressive.

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CH32V003 Provides Ultra Cheap Speech Recognition

May 30, 2024 by Jenny List 6 Comments

Speech recognition was once the stuff of science fiction, but it’s now possible with relatively modest hardware. Just how modest, you ask? How about a 10 cent microcontroller?

[Brian Smith] has achieved a very basic form of speech recognition on a CH32V003 RISC-V microcontroller. It may only recognize spoken digits, but that it does so at all on such a modest platform is impressive in itself.

For training purposes it enlists the help of a desktop Linux computer, however the recognition process is purely in the ten cent chip. He goes into much detail about how it achieves this on a system without floating point arithmetic, as well as the other shortcomings of such a limited platform.

We’ve become used to thinking of super-cheap chips as of limited use, but the truth is they’re surprisingly more capable than expected. We’re seeing them starting to appear as subsidiary processors on some badges, so it will be interesting to see them proliferate in more projects now their availability problems have eased. Go on – for ten cents, what do you have to lose?

Here’s A Plain C/C++ Implementation Of AI Speech Recognition, So Get Hackin’

November 27, 2022 by Donald Papp 19 Comments

[Georgi Gerganov] recently shared a great resource for running high-quality AI-driven speech recognition in a plain C/C++ implementation on a variety of platforms. The automatic speech recognition (ASR) model is fully implemented using only two source files and requires no dependencies. As a result, the high-quality speech recognition doesn’t involve calling remote APIs, and can run locally on different devices in a fairly straightforward manner. The image above shows it running locally on an iPhone 13, but it can do more than that.

Implementing a robust speech transcription that runs locally on a variety of devices is much easier with [Georgi]’s port of OpenAI’s *Whisper*.

[Georgi]’s work is a port of OpenAI’s Whisper model, a remarkably-robust piece of software that does a truly impressive job of turning human speech into text. Whisper is easy to set up and play with, but this port makes it easier to get the system working in other ways. Having such a lightweight implementation of the model means it can be more easily integrated over a variety of different platforms and projects.

The usual way that OpenAI’s Whisper works is to feed it an audio file, and it spits out a transcription. But [Georgi] shows off something else that might start giving hackers ideas: a simple real-time audio input example.

By using a tool to stream audio and feed it to the system every half-second, one can obtain pretty good (sort of) real-time results! This of course isn’t an ideal method, but the robustness and accuracy of Whisper is such that the results look pretty great nevertheless.

You can watch a quick demo of that in the video just under the page break. If it gives you some ideas, head over to the project’s GitHub repository and get hackin’!

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On Getting A Computer’s Attention And Striking Up A Conversation

November 14, 2022 by Maya Posch 17 Comments

With the rise in voice-driven virtual assistants over the years, the sight of people talking to various electrical devices in public and in private has become rather commonplace. While such voice-driven interfaces are decidedly useful for a range of situations, they also come with complications. One of these are the trigger phrases or wake words that voice assistants listen to when in standby. Much like in Star Trek, where uttering ‘Computer’ would get the computer’s attention, so do we have our ‘Siri’, ‘Cortana’ and a range of custom trigger phrases that enable the voice interface.

Unlike in Star Trek, however, our virtual assistants do not know when we really desire to interact. Unable to distinguish context, they’ll happily respond to someone on TV mentioning their trigger phrase. This possibly followed by a ludicrous purchase order or other mischief. The realization here is the complexity of voice-based interfaces, while still lacking any sense of self-awareness or intelligence.

Another issue is that the process of voice recognition itself is very resource-intensive, which limits the amount of processing that can be performed on the local device. This usually leads to the voice assistants like Siri, Alexa, Cortana and others processing recorded voices in a data center, with obvious privacy implications.

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OpenAI Hears You Whisper

September 22, 2022 by Al Williams 18 Comments

Should you wish to try high-quality voice recognition without buying something, good luck. Sure, you can borrow the speech recognition on your phone or coerce some virtual assistants on a Raspberry Pi to handle the processing for you, but those aren’t good for major work that you don’t want to be tied to some closed-source solution. OpenAI has introduced Whisper, which they claim is an open source neural net that “approaches human level robustness and accuracy on English speech recognition.” It appears to work on at least some other languages, too.

If you try the demonstrations, you’ll see that talking fast or with a lovely accent doesn’t seem to affect the results. The post mentions it was trained on 680,000 hours of supervised data. If you were to talk that much to an AI, it would take you 77 years without sleep!

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Need A Snack From Across Town? Send Spot!

May 2, 2022 by Dave Rowntree 22 Comments

[Dave Niewinski] clearly knows a thing or two about robots, judging from his YouTube channel. Usually the projects involve robot arms mounted on some sort of wheeled platform, but this time it’s the tune of some pretty famous yellow robot legs, in the shape of spot from Boston Dynamics. The premise is simple — tell the robot what snacks you want, entirely by voice command, and off he goes to fetch. But, we’re not talking about navigating to the fridge in the same room. We’re talking about trotting out the front door, down the street and crossing roads to visit favorite restaurant. Spot will order the snacks and bring them back, fully autonomously.

Spot’s depth cameras provide localized navigation and object avoidance information

Local AI vision system handles avoiding those pesky moving objects

There are multiple things going here, all of which are pretty big computational tasks. Firstly, there is no cloud-based voice control, ala Google voice or Alexa. The robot works on the premise of full autonomy, which means no internet connectivity for any aspect. All voice recognition, voice-to-text, and speech synthesis are performed locally using the NVIDIA Riva GPU-based AI speech SDK, running on the local NVIDIA Jetson AGX Orin carried on Spot’s back. A front-facing webcam supplies the audio feed for this. The voice recognition application listens for the wake phrase, then turns the snack order into text, for later replay when it gets to the destination. Navigation is taken care of with a Microstrain RTK GNSS module, which has all the needed robustness, such as dual antennas, and inertial fallback for those regions with a spotty signal. Navigation is no use out in the real world on its own, which is where Spot’s depth sensor cameras come in. These enable local obstacle avoidance, as per the usual spot behavior we’ve all seen before. But what about crossing the road without getting tens of thousands of dollars of someone else’s hardware crushed by a passing truck? Spot’s onboard streaming cameras are fed into the NVIDIA dash cam net AI platform which enables real-time recognition of moving obstacles such as cars, humans and anything else that might be wandering around and get in the way. All in all a cool project showing the future potential of AI in robotics for important tasks, like fetching me a beer when I most need it, even if it comes from the local corner shop.

We love robots around here. Robots can mow your lawn, navigate inside your house with a little help from invisible QR Codes, even help out with growing your food. The robot-assisted future long promised, may now be looking more like the present.

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Display Your Speech In Realtime To Help Lipreaders In The Mask Era

January 23, 2022 by Lewin Day 18 Comments

Masks are all well and good when it comes to reducing the spread of deadly pathogens, but they can make it harder to understand people when they speak. They also make lipreading impossible. [Kevin Lewis] set about building something to help.

The system consists of a small screen that can be worn on the chest or other part of the body, and a lapel microphone to record the wearer’s speech. Using the Deepgram AI speech recognition API running on a Raspberry Pi Zero W, the system decodes the speech and displays it on the Hyperpixel screen.

The API is quite capable, and can be set to only respond to the wearer’s voice, or in a group mode, display speech from multiple people in the area, displaying other voices in another colour. There’s also a translation feature using the iTranslateApp API as well.

It’s a neat tool that could be of great use in conferences or in situations where a quick simple machine translation could majorly ease communication. Video after the break.
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