Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Zhu, Pai"'
Autor:
Park, Hyun Jin, Agarwal, Dhruuv, Chen, Neng, Sun, Rentao, Partridge, Kurt, Chen, Justin, Zhang, Harry, Zhu, Pai, Bartel, Jacob, Kastner, Kyle, Wang, Gary, Rosenberg, Andrew, Wang, Quan
The keyword spotting (KWS) problem requires large amounts of real speech training data to achieve high accuracy across diverse populations. Utilizing large amounts of text-to-speech (TTS) synthesized data can reduce the cost and time associated with
Externí odkaz:
http://arxiv.org/abs/2408.10463
Autor:
Park, Hyun Jin, Agarwal, Dhruuv, Chen, Neng, Sun, Rentao, Partridge, Kurt, Chen, Justin, Zhang, Harry, Zhu, Pai, Bartel, Jacob, Kastner, Kyle, Wang, Gary, Rosenberg, Andrew, Wang, Quan
This paper explores the use of TTS synthesized training data for KWS (keyword spotting) task while minimizing development cost and time. Keyword spotting models require a huge amount of training data to be accurate, and obtaining such training data c
Externí odkaz:
http://arxiv.org/abs/2407.18879
One of the challenges in developing a high quality custom keyword spotting (KWS) model is the lengthy and expensive process of collecting training data covering a wide range of languages, phrases and speaking styles. We introduce Synth4Kws - a framew
Externí odkaz:
http://arxiv.org/abs/2407.16840
Autor:
Labrador, Beltrán, Zhu, Pai, Zhao, Guanlong, Scarpati, Angelo Scorza, Wang, Quan, Lozano-Diez, Alicia, Park, Alex, Moreno, Ignacio López
Keyword spotting systems often struggle to generalize to a diverse population with various accents and age groups. To address this challenge, we propose a novel approach that integrates speaker information into keyword spotting using Feature-wise Lin
Externí odkaz:
http://arxiv.org/abs/2311.03419
A Multilingual Keyword Spotting (KWS) system detects spokenkeywords over multiple locales. Conventional monolingual KWSapproaches do not scale well to multilingual scenarios because ofhigh development/maintenance costs and lack of resource sharing.To
Externí odkaz:
http://arxiv.org/abs/2302.12961
We propose self-training with noisy student-teacher approach for streaming keyword spotting, that can utilize large-scale unlabeled data and aggressive data augmentation. The proposed method applies aggressive data augmentation (spectral augmentation
Externí odkaz:
http://arxiv.org/abs/2106.01604
Autor:
Hard, Andrew, Partridge, Kurt, Nguyen, Cameron, Subrahmanya, Niranjan, Shah, Aishanee, Zhu, Pai, Moreno, Ignacio Lopez, Mathews, Rajiv
We demonstrate that a production-quality keyword-spotting model can be trained on-device using federated learning and achieve comparable false accept and false reject rates to a centrally-trained model. To overcome the algorithmic constraints associa
Externí odkaz:
http://arxiv.org/abs/2005.10406
Publikováno v:
In Energy Procedia November 2015 78:788-793
Publikováno v:
Petroleum; March 2023, Vol. 9 Issue: 1 p101-107, 7p
Publikováno v:
2014 12th International Conference on Signal Processing (ICSP); 2014, p1743-1748, 6p