TinyML Talks: Speech-to-intent model deployment to low-power low-footprint devices

tinyML Talks "Speech-to-intent model deployment to low-power low-footprint devices" Dmitry Maslov Product Manager Seeed Studio A traditional approach to using speech for device control/user request fulfillment is first, to transcribe the speech to text and then parse the text to the commands/quarries in suitable format. While this approach offers a lot of flexibility in terms of vocabulary and/or applications scenarios, a combination of speech recognition model and dedicated parser is not suitable for constrained resources of microcontrollers. A more efficient way is to directly parse user utterances into actionable output in form of intent/slots. In this presentation I will share techniques to train a specific domain speech-to-intent model and deploy it to Cortex M4F based development board with built-in microphone, Wio Terminal from Seeed studio.

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