Spoken content retrieval refers to directly indexing and retrieving spoken content based on
the audio rather than text descriptions. This potentially eliminates the requirement of …

In this paper, we explore the use of adversarial examples for improving a neural network
based keyword spotting (KWS) system. Specially, in our system, an effective and small …

We propose strategies for a state-of-the-art keyword search (KWS) system developed by the
SINGA team in the context of the 2014 NIST Open Keyword Search Evaluation …

Speech recognition is a sequence prediction problem. Besides employing various deep
learning approaches for frame-level classification, sequence-level discriminative training …

Serving as the tigger of a voice-enabled user interface, on-device keyword spotting model
has to be extremely compact, efficient and accurate. In this paper, we adopt a depth-wise …

A Keyword-Boosted sMBR Criterion to Enhance Keyword Search Performance in Deep Neural
Network Based Acoustic Modeling ∑ ∑ Page 1 A Keyword-Boosted sMBR Criterion to …

The IEEE Spoken Language Technology Workshop (SLT) 2021 Alpha-mini Speech
Challenge (ASC) is intended to improve research on keyword spotting (KWS) and sound …

In this paper, we proposed a method to realize the recently developed keyword-aware
grammar for LVCSR-based keyword search using weight finite-state automata (WFSA). The …

System combination (or data fusion1) is known to provide significant improvement for
spoken term detection (STD). The key issue of the system combination is how to effectively …

A keyword-sensitive language modeling framework for spoken keyword search (KWS) is
proposed to combine the advantages of conventional keyword-filler based and large …