The advent of Deep-Learning techniques and the increasing importance of personalization
in voice assistants fueled the need for open vocabulary keyword detection systems, in …

Open-vocabulary keyword spotting (KWS), which allows users to customize keywords, has
attracted increasingly more interest. However, existing methods based on acoustic models …

Conventional ASR systems use frame-level phoneme posterior to conduct force-alignment
(FA) and provide timestamps, while end-to-end ASR systems especially AED based ones …

Designing an efficient keyword spotting (KWS) system that delivers exceptional performance
on resource-constrained edge devices has long been a subject of significant attention …

Thanks to Deep Neural Networks (DNNs), the accuracy of Keyword Spotting (KWS) has
made substantial progress. However, as KWS systems are usually implemented on edge …

The paper presents a method for spoken term detection based on the Transformer
architecture. We propose the encoder-encoder architecture employing two BERT-like …

In real-world scenarios, Deep Neural Network (DNN)-powered Keyword Spotting (KWS)
systems are typically engineered as lightweight architectures, optimizing for superior …

As the advance of human-computer interaction technologies continued, keyword spotting
(KWS) systems have gained prominence in everyday devices. This study is dedicated to …

Streaming keyword spotting (KWS) is an important technique for voice assistant wake-up.
While KWS with a preset fixed keyword has been well studied, test-time customizable …

Conventional ASR systems use frame-level phoneme posterior to conduct force-alignment
(FA) and provide timestamps, while endto-end ASR systems especially AED based ones are …