Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting

Understand

Keyword spotting (KWS) constitutes a major component of human-technology interfaces.

• Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS.
• Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs).
• Inspired by large-scale state-of-the-art speech recognition systems, we combine the strengths of convolutional layers and recurrent layers to exploit local structure and long-range context.