a r t i c l e i n f o Keywords: Keyword spotting Chinese handwritten documents Word similarity Contextual word model This paper proposes a method for keyword spotting in off-line Chinese handwritten documents using a contextual word model, which measures the similarity between the query word and every candidate word in the document by combining a character classifier and the geometric context a...

This paper deals with the task of detection of a given keyword in continuous speech. We build upon a previously proposed algorithm where a modified Viterbi search algorithm is used to detect keywords, without requiring any explicit garbage or filler models. In this work, the concept of durational entropy is used to further discard a large fraction of false alarm errors. Durational entropy is de...

• We explore the impact of morphological segmentation on Keyword Spotting (KWS). ! • Handling out-of-vocabulary (OOV) words is a major challenge in KWS we aim to alleviate this problem by utilizing sub-word units.! • We augment a state-of-the-art KWS system with subword units derived from supervised and unsupervised morphological segmentations, and compare with phonetic and syllabic segmentatio...

The problem of keyword spotting in audio data has been explored for many years. Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the majority of languages in the world. This thesis addresses this lack-of-annotation problem and pres...

With the expanding development of on-device artificial intelligence, voice-enabled devices such as smart speakers, wearables, and other or edge processing systems have been proposed. However, building obtaining large training datasets that are essential for robust keyword spotting (KWS) remains cumbersome. To address this problem, we propose a deep neural network can rapidly establish high-perf...

In this paper, we propose a keyword spotting system for Korean document images and compare the proposed system with an OCR-based document retrieval system. The system is composed of character segmentation, feature extraction for the query keyword, and word-to-word matching. In the character segmentation step, we propose an effective method to resolve the connection between adjacent characters. ...

Abstract With the development of mobile smart devices, keyword spotting plays an important role in interaction between machines and users. However, low storage energy consumption devices limit accuracies tasks. Therefore, how to achieve a balance high accuracy is research hotspot for system. Convolutional neural networks have been widely adopted recent systems due their superior accuracies, suc...

In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most no-tably, person reidentification. On other hand, in area of speech recognition generated by loss are rarely used even classification problems. We fill this gap showing that combination two representation learning techniques: embedding and variant kNN ...