The IARPA BABEL program has stimulated worldwide research in keyword search
technology for low resource languages, and the NIST OpenKWS evaluations are the de …

This paper focuses on the problem of query by example spoken term detection (QbE-STD) in
zero-resource scenario. Current state-of-the-art approaches to tackle this problem rely on …

We consider the extraction of information from broadcast radio speech in Uganda for the
purposes of informing relief and development programmes by the United Nations. Although …

In this paper, we propose a novel approach to keyword search (KWS) in low-resource
languages, which provides an alternative method for retrieving the terms of interest …

Islamic Republic of Iran Broadcasting (IRIB) as one of the biggest broadcasting
organizations, produces thousands of hours of media content daily. Accordingly, the IRIB's …

The modeling of text queries as sequences of embeddings for conducting similarity
matching based search within speech features has been recently shown to improve keyword …

In particular for “low resource” Keyword Search (KWS) and Speech-to-Text (STT) tasks,
more untranscribed test data may be available than training data. Several approaches have …

In this paper, we propose a novel approach for query modeling using neural networks for
posteriorgram based keyword search (KWS). We aim to help the conventional large …

The huge amount of information stored in audio and video repositories makes search on
speech (SoS) a priority area nowadays. Within SoS, Query-by-Example Spoken Term …

Spoken term detection (STD) systems rank hypothesized detections by scores, which
indicate how confident a hypothesized detection is a true instance of the keyword. Many …