Voice Recognition has improved the quality and efficiency of human-computer and human-human interactions.
Voice Recognition has become integral for consumers and enterprises, from voice assistants to transcription. Traditionally,
Voice Recognition relied on server-based processing, requiring specialized hardware and a stable cloud connection. Voice data is sent to a 3rd party server from the device generated or stored to get accurate and reliable results. However, with the advent of
On-device Voice Recognition, users can experience server-level quality without sending voice data to a 3rd party server.
What’s On-device Voice Recognition?
On-device Voice Recognition is the technology that enables devices to process voice data directly on the device itself without sending it to a remote server. It has gained significant traction due to privacy, speed, and reliability benefits compared to server-dependent processing. Achieving highly accurate server-level quality results with additional benefits makes
On-device Voice Recognition appealing. However, despite the numerous benefits,
On-device Voice Recognition has its fair share of challenges.
Why is On-device Voice Recognition difficult?
The primary challenge is balancing compute power and memory constraints on devices and across platforms.
Voice Recognition algorithms typically require substantial computational resources, making it challenging to fit them into devices with limited hardware capabilities. To address the challenges, machine learning researchers have adopted innovative approaches to develop lightweight models that run on resource-constrained devices without compromising accuracy. However, it’s not easy. For example, OpenAI offers Whisper API, which requires voice data sent to the OpenAI servers for processing, and Whisper SDK, which processes voice data on the device. Mitchell Clark from Verge reports transcribing a 24-minute interview takes 52 mins with Whisper SDK and 8 minutes with Otter.ai. For hobby projects, 52 minutes might be acceptable. However, voice AI vendors cannot compete by being 6.5 times slower. To be fair, OpenAI communicates that the target audience of Whisper SDK is AI researchers, and Whisper API is a “much, much faster” version targeting enterprises.