SOTAVerified

Keyword Spotting

In speech processing, keyword spotting deals with the identification of keywords in utterances.

( Image credit: Simon Grest )

Papers

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Showing 301–350 of 407 papers

TitleStatusHype
AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy—0
A Bird's-eye View of Language Processing Projects at the Romanian Academy—0
A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting—0
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement—0
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons—0
AdaKWS: Towards Robust Keyword Spotting with Test-Time Adaptation—0
Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning—0
Adaptive Speech Understanding for Intuitive Model-based Spoken Dialogues—0
Advancing Airport Tower Command Recognition: Integrating Squeeze-and-Excitation and Broadcasted Residual Learning—0
Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting—0
Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting—0
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting—0
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts—0
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools—0
AI-Powered Agile Analog Circuit Design and Optimization—0
A Joint Model of Orthography and Morphological Segmentation—0
A Literature Review of Keyword Spotting Technologies for Urdu—0
Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device—0
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting—0
A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection—0
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator—0
An Alternative Deep Feature Approach to Line Level Keyword Spotting—0
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting—0
Analyzing the Representational Geometry of Acoustic Word Embeddings—0
An Anchor-Free Detector for Continuous Speech Keyword Spotting—0
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications—0
An Integrated Framework for Two-pass Personalized Voice Trigger—0
An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications—0
An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting—0
An Ultra-low Power RNN Classifier for Always-On Voice Wake-Up Detection Robust to Real-World Scenarios—0
Application of Knowledge Distillation to Multi-task Speech Representation Learning—0
A Probabilistic Framework for Lexicon-based Keyword Spotting in Handwritten Text Images—0
ASAP-FE: Energy-Efficient Feature Extraction Enabling Multi-Channel Keyword Spotting on Edge Processors—0
ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages—0
Assessing the Impact of Anisotropy in Neural Representations of Speech: A Case Study on Keyword Spotting—0
A Streaming End-to-End Framework For Spoken Language Understanding—0
AUC Optimization for Robust Small-footprint Keyword Spotting with Limited Training Data—0
AutoKWS: Keyword Spotting with Differentiable Architecture Search—0
Automatic Extraction of News Values from Headline Text—0
Automatic Speech Recognition for Humanitarian Applications in Somali—0
Automating speech reception threshold measurements using automatic speech recognition—0
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge—0
Behavior of Keyword Spotting Networks Under Noisy Conditions—0
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization—0
Boosting Tail Neural Network for Realtime Custom Keyword Spotting—0
Bridging the Gap between Audio and Text using Parallel-attention for User-defined Keyword Spotting—0
BUT QUESST 2014 System Description—0
BUT QUESST 2015 System Description—0
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting—0
Challenges and Opportunities in Multi-device Speech Processing—0
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All datasetsQUESSTGoogle Speech Commandshey SiriFKDGoogle Speech Commands V2 35TensorFlowVoxForgeGoogle Speech Commands (v2)Google Speech Commands V2 12

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NNI non-filtered(for the development set)Cnxe6.09—Unverified
2NNI Choi(for the development set)Cnxe5.89—Unverified
3NTU rnn (eval)Cnxe2.01—Unverified
4NTU dtw (eval)Cnxe2.01—Unverified
5NTU dtw (dev)Cnxe2.01—Unverified
6NTU rnn (dev)Cnxe2.01—Unverified
7ELiRF SDTW (eval)Cnxe1.19—Unverified
8ELiRF SDTW-avg (eval)Cnxe1.07—Unverified
9ELiRF SDTW (dev)Cnxe1.07—Unverified
10CUNY [Subseq+MFCC] (eval)Cnxe1.07—Unverified
#ModelMetricClaimedVerifiedStatus
1WaveFormerGoogle Speech Commands V2 1298.8—Unverified
2QNNGoogle Speech Commands V2 3598.6—Unverified
3TripletLoss-res15Google Speech Commands V1 1298.56—Unverified
4M2DGoogle Speech Commands V2 3598.5—Unverified
5EAT-SGoogle Speech Commands V2 3598.15—Unverified
6Audio Spectrogram TransformerGoogle Speech Commands V2 3598.11—Unverified
7EdgeCRNN 2.0×Google Speech Commands V2 1298.05—Unverified
8BC-ResNet-8Google Speech Commands V1 1298—Unverified
9HTS-ATGoogle Speech Commands V2 3598—Unverified
10Wav2KWSGoogle Speech Commands V1 1297.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Stacked 1D CNNError Rate1.99—Unverified
2End-to-end DNN-HMMError Rate1.7—Unverified
3HEiMDaLError Rate0.45—Unverified
#ModelMetricClaimedVerifiedStatus
1Res26Accuracy95.88—Unverified
2EfficientNet-A0 + SA + TLAccuracy95.83—Unverified
#ModelMetricClaimedVerifiedStatus
1QuaternionNeuralNetworkAccuracy (10-fold)98.53—Unverified
2SSAMBAAccuracy (10-fold)97.4—Unverified
#ModelMetricClaimedVerifiedStatus
1TensorFlow's model version 2TFMA89.7—Unverified
2TensorFlow's model version 1TFMA85.4—Unverified
#ModelMetricClaimedVerifiedStatus
12D-ConvNetAccuracy (%)95.4—Unverified
21D-ConvNetAccuracy (%)93.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Quaternion Neural NetworksAccuracy(10-fold)98.53—Unverified
#ModelMetricClaimedVerifiedStatus
1MicroNet-KWS-LAccuracy95.3—Unverified