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pro vyhledávání: '"Agarwal, Dhruuv"'
We propose GE2E-KWS -- a generalized end-to-end training and evaluation framework for customized keyword spotting. Specifically, enrollment utterances are separated and grouped by keywords from the training batch and their embedding centroids are com
Externí odkaz:
http://arxiv.org/abs/2410.16647
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