3 citations · 10 across the 8 of their papers we have counts for
10 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…
Personalizing Keyword Spotting with Speaker Information
Beltrán Labrador, Pai Zhu, Guanlong Zhao +5
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 integ…
Locale Encoding For Scalable Multilingual Keyword Spotting Models
Pai Zhu, Hyun Jin Park, Alex Park +2
A Multilingual Keyword Spotting (KWS) system detects spokenkeywords over multiple locales. Conventional monolingual KWSapproaches do not scale well to multilingual scenarios becaus…
Noisy student-teacher training for robust keyword spotting
Hyun-Jin Park, Pai Zhu, Ignacio Lopez Moreno +1
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 propo…
Training Keyword Spotting Models on Non-IID Data with Federated Learning
Andrew Hard, Kurt Partridge, Cameron Nguyen +5
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 ce…