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Listening to the World Improves Speech Command Recognition

Listening to the World Improves Speech Command Recognition

23 October 2017
B. McMahan
D. Rao
ArXivPDFHTML

Papers citing "Listening to the World Improves Speech Command Recognition"

9 / 9 papers shown
Title
Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning
Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning
Sidahmed Lachenani
Hamza Kheddar
Mohamed Ouldzmirli
40
2
0
26 Apr 2025
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on
  Riemannian Gradient Descent With Illustrations of Speech Processing
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
Jun Qi
Chao-Han Huck Yang
Pin-Yu Chen
Javier Tejedor
25
16
0
11 Mar 2022
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command
  Recognition
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition
Jun Qi
Javier Tejedor
24
4
0
11 Jan 2022
Classical-to-Quantum Transfer Learning for Spoken Command Recognition
  Based on Quantum Neural Networks
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks
Jun Qi
Javier Tejedor
39
43
0
17 Oct 2021
End-to-end Keyword Spotting using Xception-1d
End-to-end Keyword Spotting using Xception-1d
Iván Vallés-Pérez
J. Gómez-Sanchís
M. Martínez-Sober
Joan Vila-Francés
Antonio J. Serrano
E. Soria-Olivas
17
0
0
09 Oct 2021
Neural Model Reprogramming with Similarity Based Mapping for
  Low-Resource Spoken Command Recognition
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
Hao Yen
Pin-Jui Ku
Chao-Han Huck Yang
Hu Hu
Sabato Marco Siniscalchi
Pin-Yu Chen
Yu Tsao
40
4
0
08 Oct 2021
D3Net: Densely connected multidilated DenseNet for music source
  separation
D3Net: Densely connected multidilated DenseNet for music source separation
Naoya Takahashi
Yuki Mitsufuji
MedIm
17
68
0
05 Oct 2020
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech
Jakob Drachmann Havtorn
Jan Latko
Joakim Edin
Lasse Borgholt
Lars Maaløe
Lorenzo Belgrano
Nicolai Frost Jakobsen
R. Sdun
Zeljko Agic
19
3
0
02 May 2020
A neural attention model for speech command recognition
A neural attention model for speech command recognition
Douglas Coimbra de Andrade
Sabato Leo
M. Viana
Christoph Bernkopf
11
144
0
27 Aug 2018
1