Joseph Razik

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This paper presents some confidence measures for large vocabulary speech recognition which are based on word graph or N-Best List structures. More and more applications need fast estimation of any measures in order to stay real-time. We propose some simple and fast measures, locally computed, that can be directly used within the first decoding recognition(More)
State of the art of the high level feature detectors, as violent scene detectors, are supervised systems. The aim of our proposition is to show that simple non supervised confidence function derived from straightforward features can perform well compared to nowdays supervised systems for this kind of hard task. Then, we develop a violent event detector(More)
This paper presents several new confidence measures for speech recognition applications. The major advantage of these measures is that they can be evaluated with only a part of the whole sentence. Two of these measures can be computed directly within the first step of the recognition process, synchronously with the decoding engine. Such measures are useful(More)
In this paper, we introduce two new confidence measures for large vocabulary speech recognition systems. The major feature of these measures is that they can be computed without waiting for the end of the audio stream. We proposed two kinds of confidence measures: frame-synchronous and local. The frame-synchronous ones can be computed as soon as a frame is(More)
This paper presents a spermwhale' local-ization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea is to regress super-visely from these(More)