109,632 research outputs found
Phoneme Recognition Using Acoustic Events
This paper presents a new approach to phoneme recognition using nonsequential
sub--phoneme units. These units are called acoustic events and are
phonologically meaningful as well as recognizable from speech signals. Acoustic
events form a phonologically incomplete representation as compared to
distinctive features. This problem may partly be overcome by incorporating
phonological constraints. Currently, 24 binary events describing manner and
place of articulation, vowel quality and voicing are used to recognize all
German phonemes. Phoneme recognition in this paradigm consists of two steps:
After the acoustic events have been determined from the speech signal, a
phonological parser is used to generate syllable and phoneme hypotheses from
the event lattice. Results obtained on a speaker--dependent corpus are
presented.Comment: 4 pages, to appear at ICSLP'94, PostScript version (compressed and
uuencoded
Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording
In this paper we present our work on Task 1 Acoustic Scene Classi- fication
and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments
we have low-level and high-level features, classifier optimization and other
heuristics specific to each task. Our performance for both tasks improved the
baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9%
compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based
Error Rate of 0.76 compared to the baseline of 0.91
Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models
Automatic emotion recognition from speech has been recently focused on the prediction of time-continuous dimensions (e.g., arousal and valence) of spontaneous and realistic expressions of emotion, as found in real-life interactions. However, the automatic prediction of such emotions poses several challenges, such as the subjectivity found in the definition of a gold standard from a pool of raters and the issue of data scarcity in training models. In this work, we introduce a novel emotion recognition system, based on ensemble of single-speaker-regression-models (SSRMs). The estimation of emotion is provided by combining a subset of the initial pool of SSRMs selecting those that are most concordance among them. The proposed approach allows the addition or removal of speakers from the ensemble without the necessity to re-build the entire machine learning system. The simplicity of this aggregation strategy, coupled with the flexibility assured by the modular architecture, and the promising results obtained on the RECOLA database highlight the potential implications of the proposed method in a real-life scenario and in particular in WEB-based applications
New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component
License Plate recognition plays an important role on the traffic monitoring
and parking management systems. In this paper, a fast and real time method has
been proposed which has an appropriate application to find tilt and poor
quality plates. In the proposed method, at the beginning, the image is
converted into binary mode using adaptive threshold. Then, by using some edge
detection and morphology operations, plate number location has been specified.
Finally, if the plat has tilt, its tilt is removed away. This method has been
tested on another paper data set that has different images of the background,
considering distance, and angel of view so that the correct extraction rate of
plate reached at 98.66%.Comment: 3rd IEEE International Conference on Computer and Knowledge
Engineering (ICCKE 2013), October 31 & November 1, 2013, Ferdowsi Universit
Mashha
First impressions: A survey on vision-based apparent personality trait analysis
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft
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