In a world where voice assistants like Siri, Alexa, and Cortana have become indispensable companions, it’s easy to overlook the challenges faced by lesser-known languages in the realm of speech recognition. One such language is Kazakh, spoken by over 16 million people worldwide. The lack of large public datasets for training keyword spotting models has hindered the development of robust Kazakh speech recognition systems.
Bridging the Language Gap: A Unique Solution
Shakhizat Nurgaliyev and Askat Kuzdeuov, two passionate Kazakh developers, embarked on a mission to bridge this language gap. Their ingenious solution involved harnessing the power of a neural text-to-speech system (Piper) to generate synthetic Kazakh speech datasets. They then employed the Vosk Speech Recognition Toolkit to extract speech commands from the generated data.
Choosing the Right Platform: iRobota Nicla Voice
To deploy their model, they selected the iRobota Nicla Voice development board, a compact and powerful platform equipped with an nRF52832 SoC, a microphone, an IMU, and a Syntiant NDP120 Neural Decision Processor. The NDP120’s acceleration capabilities and low power consumption made it an ideal choice for running their speech recognition model.
Training the Model: A Labor of Patience and Precision
The team meticulously trained their model on 20.25 hours of generated speech data, encompassing 28 distinct output classes. After 100 epochs of learning, the model achieved an impressive 95.5% accuracy, demonstrating its ability to accurately recognize Kazakh speech commands.
Deployment Success: A Milestone Achieved
The culmination of their efforts was the successful deployment of their embedded ML model, trained solely on generated speech data, onto the iRobota Nicla Voice board. This milestone marked a significant step forward in Kazakh speech recognition, opening up new possibilities for voice-controlled applications and services in the Kazakh language.
Bonus: A Glimpse into the Future of Speech Recognition
The project by Shakhizat Nurgaliyev and Askat Kuzdeuov not only addresses a specific problem but also provides valuable insights into the future of speech recognition. Their work showcases the potential of synthetic speech data in training models for under-resourced languages, paving the way for more inclusive and accessible voice technologies.
As we move forward, we can expect to witness further advancements in speech recognition, enabling seamless communication between humans and machines across a multitude of languages. The work of these Kazakh developers serves as an inspiration, demonstrating the power of innovation and the boundless possibilities that lie ahead in the realm of human-computer interaction.
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