Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-
quality results, but the key challenge of how to semantically align two embeddings for multi …

Spotting user-defined/flexible keywords represented in text frequently uses an expensive
text encoder for joint analysis with an audio encoder in an embedding space, which can …

This study presents a novel zero-shot user-defined keyword spotting model that utilizes the
audio-phoneme relationship of the keyword to improve performance. Unlike the previous …

The recognition of rare named entities, such as personal names and terminologies, is
challenging for automatic speech recognition (ASR) systems, especially when they are not …

Open vocabulary keyword spotting is a crucial and challenging task in automatic speech
recognition (ASR) that focuses on detecting user-defined keywords within a spoken …

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

Identifying keywords in an open-vocabulary context is crucial for personalizing interactions
with smart devices. Previous approaches to open vocabulary keyword spotting depend on a …

Acoustic word embeddings (AWEs) are vector representations of spoken words. An effective
method for obtaining AWEs is the Correspondence Auto-Encoder (CAE). In the past, the …

Customizable keyword spotting (KWS) in continuous speech has attracted increasing
attention due to its real-world application potential. While contrastive learning (CL) has been …

For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset
containing massive target keywords has known to be essential to generalize to arbitrary …