Keyword Spotting
95 papers with code • 10 benchmarks • 8 datasets
In speech processing, keyword spotting deals with the identification of keywords in utterances.
( Image credit: Simon Grest )
Benchmarks
These leaderboards are used to track progress in Keyword Spotting
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Libraries
Use these libraries to find Keyword Spotting models and implementationsDatasets
Speech Commands
TAU Urban Acoustic Scenes 2019
VoxForge
PodcastFillers
EmoSpeech
Most implemented papers
An End-to-End Architecture for Keyword Spotting and Voice Activity Detection
mindorii/kws
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We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection.
Attention-based End-to-End Models for Small-Footprint Keyword Spotting
In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system.
Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
In addition, we release the implementation of the proposed and the baseline models including an end-to-end pipeline for training models and evaluating them on mobile devices.
AST: Audio Spectrogram Transformer
YuanGongND/ast
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In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels.
Trainable Frontend For Robust and Far-Field Keyword Spotting
asteroid-team/asteroid-filterbanks
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Robust and far-field speech recognition is critical to enable true hands-free communication.
Howl: A Deployed, Open-Source Wake Word Detection System
castorini/howl
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EMNLP (NLPOSS) 2020
We describe Howl, an open-source wake word detection toolkit with native support for open speech datasets, like Mozilla Common Voice and Google Speech Commands.
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
huckiyang/speech_quantum_dl
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Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization
This paper introduces neural architecture search (NAS) for the automatic discovery of small models for keyword spotting (KWS) in limited resource environments.
EfficientNet-Absolute Zero for Continuous Speech Keyword Spotting
AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
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To this end, the football keyword dataset (FKD), as a new keyword spotting dataset in Persian, is collected with crowdsourcing.
Few-Shot Keyword Spotting in Any Language
harvard-edge/multilingual_kws
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With just five training examples, we fine-tune the embedding model for keyword spotting and achieve an average F1 score of 0. 75 on keyword classification for 180 new keywords unseen by the embedding model in these nine languages.
