5 citations · 13 across the 7 of their papers we have counts for
7 papers
LLM-Synth4KWS: Scalable Automatic Generation and Synthesis of Confusable Data for Custom Keyword Spotting
Pai Zhu, Quan Wang, Dhruuv Agarwal +1
Custom keyword spotting (KWS) allows detecting user-defined spoken keywords from streaming audio. This is achieved by comparing the embeddings from voice enrollments and input audi…
GraphemeAug: A Systematic Approach to Synthesized Hard Negative Keyword Spotting Examples
Harry Zhang, Kurt Partridge, Pai Zhu +4
Spoken Keyword Spotting (KWS) is the task of distinguishing between the presence and absence of a keyword in audio. The accuracy of a KWS model hinges on its ability to correctly c…
GE2E-KWS: Generalized End-to-End Training and Evaluation for Zero-shot Keyword Spotting
Pai Zhu, Jacob W. Bartel, Dhruuv Agarwal +3
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…
Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting
Hyun Jin Park, Dhruuv Agarwal, Neng Chen +10
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-speec…
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model
Hyun Jin Park, Dhruuv Agarwal, Neng Chen +10
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 amou…
Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments
Pai Zhu, Dhruuv Agarwal, Jacob W. Bartel +3
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 lan…
Mixed Federated Learning: Joint Decentralized and Centralized Learning
Sean Augenstein, Andrew Hard, Lin Ning +4
Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixe…