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 …

We adopt a Dirichlet process Gaussian mixture model (DPGMM) for unsupervised acoustic
modeling and represent speech frames with Gaussian posteriorgrams. The model performs …

We propose to learn acoustic word embeddings with temporal context for query-by-example
(QbE) speech search. The temporal context includes the leading and trailing word …

We propose a framework which ports Dirichlet Gaussian mixture model (DPGMM) based
labels to deep neural network (DNN). The DNN trained using the unsupervised labels is …

We propose to use a feature representation obtained by pairwise learning in a low-resource
language for query-by-example spoken term detection (QbE-STD). We assume that word …

The large amount of information stored in audio and video repositories makes search on
speech (SoS) a challenging area that is continuously receiving much interest. Within SoS …

We use query-by-example keyword spotting (QbyE-KWS) approach to solve the
personalized wake-up word detection problem for small-footprint, low-computational cost on …

Query-by-Example approach of spoken content retrieval has gained much attention because
of its feasibility in the absence of speech recognition and its applicability in a multilingual …

In this paper we describe the system proposed by NNI (NWPUNTU-I2R) team for the
QUESST task within the Mediaeval 2014 evaluation. To solve the problem, we used both …

This paper documents the significant components of a state-ofthe-art language-independent
query-by-example spoken term detection system designed for the Query by Example Search …