Creating Deep Learning-Based Speech Products in Record Time
Samer Hijazi, Ph.D, BabbleLabs
In the past two years, we’ve seen the industry discover speech as a critical interface protocol between humans and machines, especially for cloud-based information queries driven by speech recognition. However, speech recognition is just the tip of the iceberg. A whole new set of functions—speech enhancement, speaker identification and authentication, and background noise classification—are becoming available. These create new and significant opportunities for every application that touches audio or video—opening new potential for improved intelligibility, personalization, and customer “stickiness.”
BabbleLabs Clear Cloud is an example of a breakthrough deep learning technology applied to widely applicable speech APIs and it gives us a sense of the future roadmap of speech-centric applications. The number of speech problems BabbleLabs is working on is growing by the day, and the company has to develop a flow that will maximize the speed of creating production-ready SW IP. Using mature and comprehensive toolboxes from MathWorks, such as DSP System Toolbox™, Deep Learning Toolbox™ (formerly Neural Network Toolbox™), and MATLAB Coder™, BabbleLabs can create state-of-the-art SW IP products in record time. These SW IP products integrate advanced digital signal processing (DSP) and sophisticated deep learning architectures using a homogeneous flow from development to deployment.
Deep Learning Toolbox™ (formerly Neural Network Toolbox™)
Recorded: 5 Nov 2018
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