9,085 research outputs found
Mostly-Unsupervised Statistical Segmentation of Japanese Kanji Sequences
Given the lack of word delimiters in written Japanese, word segmentation is
generally considered a crucial first step in processing Japanese texts. Typical
Japanese segmentation algorithms rely either on a lexicon and syntactic
analysis or on pre-segmented data; but these are labor-intensive, and the
lexico-syntactic techniques are vulnerable to the unknown word problem. In
contrast, we introduce a novel, more robust statistical method utilizing
unsegmented training data. Despite its simplicity, the algorithm yields
performance on long kanji sequences comparable to and sometimes surpassing that
of state-of-the-art morphological analyzers over a variety of error metrics.
The algorithm also outperforms another mostly-unsupervised statistical
algorithm previously proposed for Chinese.
Additionally, we present a two-level annotation scheme for Japanese to
incorporate multiple segmentation granularities, and introduce two novel
evaluation metrics, both based on the notion of a compatible bracket, that can
account for multiple granularities simultaneously.Comment: 22 pages. To appear in Natural Language Engineerin
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
A Novel Method For Speech Segmentation Based On Speakers' Characteristics
Speech Segmentation is the process change point detection for partitioning an
input audio stream into regions each of which corresponds to only one audio
source or one speaker. One application of this system is in Speaker Diarization
systems. There are several methods for speaker segmentation; however, most of
the Speaker Diarization Systems use BIC-based Segmentation methods. The main
goal of this paper is to propose a new method for speaker segmentation with
higher speed than the current methods - e.g. BIC - and acceptable accuracy. Our
proposed method is based on the pitch frequency of the speech. The accuracy of
this method is similar to the accuracy of common speaker segmentation methods.
However, its computation cost is much less than theirs. We show that our method
is about 2.4 times faster than the BIC-based method, while the average accuracy
of pitch-based method is slightly higher than that of the BIC-based method.Comment: 14 pages, 8 figure
Automatic Segmentation of Spontaneous Data using Dimensional Labels from Multiple Coders
This paper focuses on automatic segmentation of spontaneous data using continuous dimensional labels from multiple coders. It introduces efficient algorithms to the aim of (i) producing ground-truth by maximizing inter-coder agreement, (ii) eliciting the frames or samples that capture the transition to and from an emotional state, and (iii) automatic segmentation of spontaneous audio-visual data to be used by machine learning techniques that cannot handle unsegmented sequences. As a proof of concept, the algorithms introduced are tested using data annotated in arousal and valence space. However, they can be straightforwardly applied to data annotated in other continuous emotional spaces, such as power and expectation
Filtering the Unknown: Speech Activity Detection in Heterogeneous Video Collections
In this paper we discuss the speech activity detection system that we used for detecting speech regions in the Dutch TRECVID video collection. The system is designed to filter non-speech like music or sound effects out of the signal without the use of predefined non-speech models. Because the system trains its models on-line, it is robust for handling out-of-domain data. The speech activity error rate on an out-of-domain test set, recordings of English conference meetings, was 4.4%. The overall error rate on twelve randomly selected five minute TRECVID fragments was 11.5%
Semantic analysis of field sports video using a petri-net of audio-visual concepts
The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports
video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework
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