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Precise [1], Snowboy [2], and Porcupine [3] are all designed to work offline. [1] https://github.com/MycroftAI/mycroft-precise [2] https://github.com/kitt-ai/snowboy [3] https://github.com/Picovoice/porcupine
Later I worked on the Microsoft Kinect with its 4-microphone array. With only a single microphone, it's so much harder to filter out background noise. If you don't find a system based on multiple microphones, I don't believe you can be successful if there's any ongoing noise (dishwasher, loud fans, etc), but a system that works in only quiet conditions is possible.
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For a software engineer with no experience in machine learning / AI, what does it mean to build your own language model? Does it require coding? Hundreds of hours of audio data from your own voice? A significant amount of computing power?
The tools available means you only need to provide a list of normal sentences, and they should include the words you'd like it to know about. For my case I just wanted to train it on like 30 different sentences, that took less than a second. But for a general assistant ala Google Home you'll want a large number of sentences and I hear it can take a while (hour or few?). Due to using probabilities it will match words…
This was on a slightly earlier version, and they've made improvements in speed and quality of recognition since then.
Mozilla DeepSpeech trained on the Common Voice dataset for English. You can get pretrained models too. They have a nice matrix channel where you can get help, and pretty good documentation. It is also actively developed by several engineers. http://voice.mozilla.org/en/datasets and http://github.com/mozilla/DeepSpeech/
Interesting. Are there any projects actively using this today?
https://github.com/DragonComputer/Dragonfire
There's a minimal demo app I put together here too: https://github.com/nmstoker/SimpleSpeechLoop
Disclaimer: I am the founder of Hiome, a smart home startup focused on private by design local-only products. What actions are you looking to handle with the assistant? Reason I ask is because a voice assistant is a command line interface with no auto-complete or visual feedback. It doesn’t scale well as you add more devices or commands to your home, because it becomes impossible to remember all the phrases you progr…
Would you share the sensor(s) you're using in the device?
KaldiAG has an English model available, but other models could be trained. Although you can't just drop in and use a standard Kaldi model with KaldiAG, the modifications required are fairly minimal and don't require any training or modification of its acoustic model. All recognition is performed locally and off line by default, but you can also selectively choose to do some recognition in the cloud, too.
Kaldi generally performs at the state of art. As a hybrid engine, although training can be more complicated, it generally requires far less training data to achieve high accuracy, compared to "end to end" engines.
TL; DR: Win 10 IoT for RasPi does it. ----------------- Windows 10 IoT for Raspberry Pi comes with offline speech recognition API. It was not hard to slap some code together that turns on a light when someone says "banana" at a hackathon. Sounds like exactly what you need. >If it helps I'm happy to reduce vocabulary to a dictionary of words You will do it with an XML grammar file for offline recognition[4]. [1] https…
This is really interesting, but I have a few questions: - The setup guide shows a Windows system making a Windows iot version. Can't I just download an iso and flash it to an sdcard with dd? Does it need a license? - The demo projects show C# and while I can develop in monodevelop, I don't have a Windows to compile it with. Is a C# compiler included in Windows iot's .NET distribution or are there also cross-platform…
TL; DR: Win 10 IoT for RasPi does it. ----------------- Windows 10 IoT for Raspberry Pi comes with offline speech recognition API. It was not hard to slap some code together that turns on a light when someone says "banana" at a hackathon. Sounds like exactly what you need. >If it helps I'm happy to reduce vocabulary to a dictionary of words You will do it with an XML grammar file for offline recognition[4]. [1] https…
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Yes, for a number of European and some Asian languages we can do this; it's mainly the question of business opportunity.
That's very cool. Did you somehow solve autogenerating a training corpus for a new language as long as it's popular? Based on my impressions working with other people in the space, coming up with the training data seems like a big bottleneck - as the available engines are pretty good at learning new languages already.