Keyword Spotting
96 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
Latest papers
TACos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping
To segment a signal into blocks to be analyzed, few-shot keyword spotting (KWS) systems often utilize a sliding window of fixed size.
Semi-Supervised Federated Learning for Keyword Spotting
diaoenmao/Semi-Supervised-Federated-Learing-for-Keyword-Spotting
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Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants.
Plug-and-Play Multilingual Few-shot Spoken Words Recognition
fewshotml/plix
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As technology advances and digital devices become prevalent, seamless human-machine communication is increasingly gaining significance.
Unsupervised Speech Representation Pooling Using Vector Quantization
IIP-Sogang/speech-pooling-benchmark
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However, the pooling problem remains; the length of speech representations is inherently variable.
AraSpot: Arabic Spoken Command Spotting
Spoken keyword spotting (KWS) is the task of identifying a keyword in an audio stream and is widely used in smart devices at the edge in order to activate voice assistants and perform hands-free tasks.
LipLearner: Customizable Silent Speech Interactions on Mobile Devices
Silent speech interface is a promising technology that enables private communications in natural language.
ASiT: Local-Global Audio Spectrogram vIsion Transformer for Event Classification
sara-ahmed/asit
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Transformers, which were originally developed for natural language processing, have recently generated significant interest in the computer vision and audio communities due to their flexibility in learning long-range relationships.
BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance
htqin/bifsmnv2
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We highlight that benefiting from the compact architecture and optimized hardware kernel, BiFSMNv2 can achieve an impressive 25. 1x speedup and 20. 2x storage-saving on edge hardware.
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
wngh1187/ipet
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To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain.
MAST: Multiscale Audio Spectrogram Transformers
Sreyan88/LAPE
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We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST).
