IoT: GenAI voice helps generate speech recognition models

A new generative AI feature brings voice recognition to tiny devices with a text-to-speech (TTS) synthetic dataset generation capability. It enables developers to generate synthetic speech data with greater precision and tailor voice attributes like pitch, cadence, and tone to meet specific application requirements.

SensiML, a subsidiary of QuickLogic, has incorporated this generative AI feature into Data Studio, its dataset management application for Internet of Things (IoT) edge devices. This new feature will allow embedded device developers to utilize TTS and AI voice generation to rapidly create hyper-realistic synthetic speech datasets that are essential for building robust keyword recognition, voice command, and speaker identification models.

The new TTS and AI voice generation feature enables seamless integration into existing Data Studio workflows. Source: SensiML

This genAI capability aims to eliminate the time-consuming and costly process of manually recording phrases from large populations of diverse speakers and thus accelerate the time-to-market for voice-enabled IoT devices. “Developers can now harness synthetic speech technology to create highly accurate and diverse training datasets, accelerating the deployment of intelligent voice-controlled applications directly on microcontrollers,” said Chris Rogers, CEO of SensiML.

To understand how it works, let’s take the example of a home security system that uses voice commands for activation and status updates. This text-to-speech and AI voice generator feature will allow developers to efficiently create extensive voice datasets, enabling the system to recognize a wide range of user commands accurately.

Moreover, it allows developers to custom-build their own ML code for IoT devices needing to handle complex voice and sound recognition tasks directly on-device without the need for constant connectivity or high computational power. That’s crucial for applications operating in environments where connectivity may be inconsistent and where fast, reliable processing is critical.

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