arXiv:1710.06554
공식 코드 · Papers with Code
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
원문 초록
We describe Honk, an open-source PyTorch reimplementation of convolutional neural networks for keyword spotting that are included as examples in TensorFlow. These models are useful for recognizing "command triggers" in speech-based interfaces (e.g., "Hey Siri"), which serve as explicit cues for audio recordings of utterances that are sent to the cloud for full speech recognition. Evaluation on Google's recently released Speech Commands Dataset shows that our reimplementation is comparable in accuracy and provides a starting point for future work on the keyword spotting task.
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Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing
First Steps Towards Voice Anonymization for Code-Switching Speech
MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
Splitformer: An improved early-exit architecture for automatic speech recognition on edge devices
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia
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