This paper presents an application of nonlinear neural networks to topic spotting. Neural networks allow us to model higher-order interaction between document terms and to simultaneously predict multiple topics using shared hidden features. In the context of this model, we compare two approaches to dimensionality reduction in representation: one based on term selection and another based on Late...

Keyword spotting (or spoken term detection) is an interesting task in Music Information Retrieval that can be applied to a number of problems. Its purposes include topical search and improvements for genre classification. Keyword spotting is a well-researched task on pure speech, but state-of-the-art approaches cannot be easily transferred to singing because phoneme durations have much higher v...

This paper presents and discusses keyword spotting methods for searching in speech. In contrast with searching in text, the searching in speech or generally in multimedia data still represents a challenge. The aim of the paper is to present a keyword spotting (KWS) method based on a large vocabulary continuous speech recognition (LVCSR) system, based on phonetics decoder, and keyword spotting u...

This paper is concerned with the problem of phonetic modeling in a Mandarin keyword spotting system. The task is to detect 20 keywords from continuous speech in the Call Home corpus from the Linguistic Data Consortium (LDC). Different speech units are explored, including whole word, syllable, and demi-syllable (INITIAL and FINAL). In our speaker-independent HMM-based Mandarin keyword spotting e...

Some rotors are bent permanently due to high operating temperatures, repeated transition periods, and so on. Rotors with large deformations often require straightening processes. The goal of this study is develop a method determine the optimal locations strengths hammer peening for gas turbine rotors. A set parametric simulations were performed various dimensions straight locations. deformed ge...

Word spotting is an important recognition task in historical document analysis. In most cases methods are developed and evaluated assuming perfect word segmentations. In this paper we propose an experimental framework to quantify the effect of goodness of word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of genera...

This paper addresses the problem of word spotting in handwritten documents. We propose a coarse-to-fine segmentation free approach. This approach is based on two filtering phases, which are a global filtering followed by a local filtering after changing the observation scale. The contribution of this work is the use and the adaptation of the Haarlike-features in word spotting task for each test...

Spontaneous speech includes many sentences that fall outside the task domain. Furthermore, the boundary between sentences is often unclear in spontaneous speech because of the likes of corrections, stammering or overlap with the next utterance. We previously developed a sentence spotting system that uses Vector ContinuousDynamic Programming (VCDP). This system works well for sentence spotting i...

Word spotting, or keyword identification, is a highly challenging task when there are multiple speakers speaking simultaneously. In the case of a game being controlled by children solely through voice, the task becomes extremely difficult. Children, unlike adults, typically do not await their turn to speak in an orderly fashion. They interrupt and shout at arbitrary times, speak or say things t...