7,373 research outputs found
Segmenting DNA sequence into words based on statistical language model
This paper presents a novel method to segment/decode DNA sequences based on n-gram statistical language model. Firstly, we find the length of most DNA “words” is 12 to 15 bps by analyzing the genomes of 12 model species. The bound of language entropy of DNA sequence is about 1.5674 bits. After building an n-gram biology languages model, we design an unsupervised ‘probability approach to word segmentation’ method to segment the DNA sequences. The benchmark of segmenting method is also proposed. In cross segmenting test, we find different genomes may use the similar language, but belong to different branches, just like the English and French/Latin. We present some possible applications of this method at last
A Speech Recognizer based on Multiclass SVMs with HMM-Guided Segmentation
Automatic Speech Recognition (ASR) is essentially a problem of pattern
classification, however, the time dimension of the speech signal has
prevented to pose ASR as a simple static classification problem. Support
Vector Machine (SVM) classifiers could provide an appropriate solution,
since they are very well adapted to high-dimensional classification problems.
Nevertheless, the use of SVMs for ASR is by no means straightforward,
mainly because SVM classifiers require an input of fixed-dimension.
In this paper we study the use of a HMM-based segmentation as a mean to
get the fixed-dimension input vectors required by SVMs, in a problem of
isolated-digit recognition. Different configurations for all the parameters
involved have been tested. Also, we deal with the problem of multi-class
classification (as SVMs are initially binary classifers), studying two of the
most popular approaches: 1-vs-all and 1-vs-1
Dictionary-based lip reading classification
Visual lip reading recognition is an essential stage in many multimedia systems such as âAudio Visual Speech
Recognitionâ [6], âMobile Phone Visual System for deaf peopleâ, âSign Language Recognition Systemâ, etc.
The use of lip visual features to help audio or hand recognition is appropriate because this information is robust
to acoustic noise. In this paper, we describe our work towards developing a robust technique for lip reading
classification that extracts the lips in a colour image by using EMPCA feature extraction and k-nearest-neighbor
classification. In order to reduce the dimensionality of the feature space the lip motion is characterized by three
templates that are modelled based on different mouth shapes: closed template, semi-closed template, and wideopen
template. Our goal is to classify each image sequence based on the distribution of the three templates and
group the words into different clusters. The words that form the database were grouped into three different
clusters as follows: group1(âIâ, âhighâ, âlieâ, âhardâ, âcardâ, âbyeâ), group2(âyou, âoweâ, âwordâ), group3(âbirdâ)
SVMs for Automatic Speech Recognition: a Survey
Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact.
During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed.
These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research
Robust ASR using Support Vector Machines
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units.
In this paper, we have compared two approaches in noisy environments: first, a hybrid HMMâSVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841â1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad
A Labeled Graph Kernel for Relationship Extraction
In this paper, we propose an approach for Relationship Extraction (RE) based
on labeled graph kernels. The kernel we propose is a particularization of a
random walk kernel that exploits two properties previously studied in the RE
literature: (i) the words between the candidate entities or connecting them in
a syntactic representation are particularly likely to carry information
regarding the relationship; and (ii) combining information from distinct
sources in a kernel may help the RE system make better decisions. We performed
experiments on a dataset of protein-protein interactions and the results show
that our approach obtains effectiveness values that are comparable with the
state-of-the art kernel methods. Moreover, our approach is able to outperform
the state-of-the-art kernels when combined with other kernel methods
- âŚ