Speech recognition on Arm Cortex-M by Fluent.ai

Introduction

Introduction

Good morning, good afternoon, and good evening! In today's virtual tech talk series, Wei, from the Machine Learning Ecosystem at Arm, moderated a session focusing on speech recognition on Arm Cortex-M microcontrollers. The presentation featured Fluent.ai, an ecosystem partner, discussing their innovative solutions in the speech recognition industry.

Agenda

The agenda included an overview of the company, detailed insights into Fluent.ai's technology, particularly its unique MicroCore framework, and live demonstrations of their speech recognition capabilities. There were also discussions about future presentations related to Arm CPU, GPU, and MPUs, as well as the significance of synthetic data in AI.

About Fluent.ai

Founded in 2015, Fluent.ai emerged from years of research aiming to make speech recognition accessible in various languages. Their technology stands out by focusing on local processing rather than cloud dependency, allowing it to operate efficiently on low-power devices.

Innovative Technology

Fluent.ai has developed a unique end-to-end spoken language understanding technology that processes speech directly into meaning without requiring transcription into text. This method drastically reduces training data needs and allows for compact models that can run efficiently on low-power Arm Cortex-M microcontrollers. One of the standout advantages of this approach is its ability to handle multilingual commands simultaneously.

Product Offerings

Fluent.ai's two main products include:

1. Wake Word Detection: Able to listen for multiple trigger phrases simultaneously, optimized for low power and fast response latency.
2. Automatic Intent Recognition (AIR): Directly converts speech to intent on-device; supports multiple languages and hundreds of commands.

During the tech talk, there was a demonstration running a Cortex-M33 microcontroller, showcasing the detection of various wake words and commands in real time.

Fluent.ai MicroCore

Fluent.ai's MicroCore is the foundation of their speech recognition system for low-resource microcontrollers. It uses efficient signal processing techniques to ensure real-time performance even on devices with limited computational resources. The architecture leverages the CMSIS-NN library for optimized processing on Arm devices.

Importance of Noise Handling

Noise cancellation is critical in the speech recognition space. Fluent.ai addresses challenges through specially designed algorithms that enhance noise robustness in their speech recognition engine. Moreover, by using signal processing techniques with multiple microphones, they effectively reduce interference from background noise.

Security Considerations

Fluent.ai emphasizes device security by keeping all processing on-device, ensuring user privacy and security. This approach minimizes the risk of data breaches that can potentially arise from cloud data processing.

Demo and Results

The session concluded with a demo showing real-time operation of their smart home assistant capabilities on an Arm Cortex-M7 device. It illustrated the ability to recognize and act upon various commands issued in different languages, completely managed within the device itself.

Conclusion

Fluent.ai is paving the way for efficient and effective speech recognition solutions tailored for low-power applications, enabling innovation across multiple industries and use cases.

Keywords

Speech recognition, Arm Cortex-M, Fluent.ai, MicroCore, low-power devices, wake word detection, automatic intent recognition, multilingual support, noise cancellation, security.

FAQ

Q: How does Fluent.ai's technology differ from traditional speech recognition technologies?
A: Fluent.ai uses an end-to-end spoken language understanding approach that processes speech directly into meaning without needing transcription, allowing for lower power consumption and better efficiency.

Q: What are the minimum hardware requirements for running Fluent.ai's solution?
A: The technology can run on a Cortex-M4 at as low as 30 MHz with around 100 KB of RAM, supporting multiple wake words and intents.

Q: How does Fluent.ai handle background noise in speech recognition?
A: Fluent.ai employs specialized algorithms during model training and utilizes signal processing techniques to filter out background noise.

Q: Is user personalization possible with Fluent.ai's speech recognition solution?
A: Yes, Fluent.ai offers personalization features, such as user trainable wake words, allowing devices to adapt to individual user preferences.

Q: What security measures are in place for devices running Fluent.ai's technology?
A: All processing occurs on the device itself, minimizing risks associated with cloud-based data handling and enhancing privacy for users.