Book review

Kiss Álmos Péter (ed.), Az afrikai terrorista- és szakadárszervezetek (African Terrorist and Secessionist Organizations), HVK TKH, Budapest, 2015. ISBN 978-963-89948-4-4 by András Kocso

An on-line speaker adaptation method for HMM-based speech recognizers

In the past few years numerous techniques have been proposed to improve the efficiency of basic adaptation methods like MLLR and MAP. These adaptation methods have a common aim, which is to increase the likelihood of the phoneme models for a particular speaker. During their operation, these speaker adaptation methods need precise phonetic segmentation information of the actual utterance, but these data samples are often faulty. To improve the overall performance, only those frames from the spoken sentence which are well segmented should be retained, while the incorrectly segmented data should not be used during adaptation. Several heuristic algorithms have been proposed in the literature for the selection of the reliably segmented data blocks, and here we would like to suggest some new heuristics that discriminate between faulty and well-segmented data. The effect of these methods on the efficiency of speech recognition using speaker adaptation is examined, and conclusions for each will be drawn. Besided post-filtering the set of the segmented adaptation examples, another way of improving the efficiency of the adaptation method might be to create a more precise segmentation, which should then reduce the chance of faulty data samples being included. We suggest a method like this here as well which is based on a scoring procedure for the N-best lists, taking into account phoneme duration

Robust clustering - based realtime vowel recognition

In the therapy of the hearing impaired one of the key problems is how to deal with the lack of proper auditive feedback which impedes the development of intelligible speech. The effectiveness of the therapy relies heavily on accurate phoneme recognition. Because of the environmental difficulties, simple recognition algorithms may have a weak classification performance, so various techniques such as normalization and classifier combination are applied to raising the overall recognition accuracy. In earlier work we came to realise that the classification accuracy is higher on a database that is manually clustered according to the gender and age of the speakers. This paper examines what happens when we cluster the database into a few groups automatically and then we train separate classifiers for each cluster. The results shows that this two-step method can increase the recognition performance by several percent

Classification using a sparse combination of basis functions

Combinations of basis functions are applied here to generate and solve a convex reformulation of several well-known machine learning algorithms like certain variants of boosting methods and Support Vector Machines. We call such a reformulation a Convex Networks (CN) approach. The nonlinear Gauss-Seidel iteration process for solving the CN problem converges globally and fast as we prove. A major property of CN solution is the sparsity, the number of basis functions with nonzero coefficients. The sparsity of the method can effectively be controlled by heuristics where our techniques are inspired by the methods from linear algebra. Numerical results and comparisons demonstrate the effectiveness of the proposed methods on publicly available datasets. As a consequence, the CN approach can perform learning tasks using far fewer basis functions and generate sparse solutions

A hierarchical evaluation methodology in speech recognition

In speech recognition vast hypothesis spaces are generated, so the search methods used and their speedup techniques are both of great importance. One way of getting a speedup gain is to search in multiple steps. In this multipass search technique the first steps use only a rough estimate, while the latter steps apply the results of the previous ones. To construct these raw tests we use simplified phoneme groups which are based on some distance function defined over phonemes. The tests we performed show that this technique could significantly speed up the recognition process

Various hyperplane classifiers using kernel feature spaces

In this paper we introduce a new family of hyperplane classifiers. But, in contrast to Support Vector Machines (SVM) - where a constrained quadratic optimization is used - some of the proposed methods lead to the unconstrained minimization of convex functions while others merely require solving a linear system of equations. So that the efficiency of these methods could be checked, classification tests were conducted on standard databases. In our evaluation, classification results of SVM were of course used as a general point of reference, which we found were outperformed in many cases

Extracting human protein information from MEDLINE using a full-sentence parser

Today, a fair number of systems are available for the task of processing biological data. The development of effective systems is of great importance since they can support both the research and the everyday work of biologists. It is well known that biological databases are large both in size and number, hence data processing technologies are required for the fast and effective management of the contents stored in databases like MEDLINE. A possible solution for content management is the application of natural language processing methods to help make this task easier. With our approach we would like to learn more about the interactions of human genes using full-sentence parsing. Given a sentence, the syntactic parser assigns to it a syntactic structure, which consists of a set of labelled links connecting pairs of words. The parser also produces a constituent representation of a sentence (showing noun phrases, verb phrases, and so on). Here we show experimentally that using the syntactic information of each abstract, the biological interactions of genes can be predicted. Hence, it is worth developing the kind of information extraction (IE) system that can retrieve information about gene interactions just by using syntactic information contained in these text. Our IE system can handle certain types of gene interactions with the help of machine learning (ML) methodologies (Hidden Markov Models, Artificial Neural Networks, Decision Trees, Support Vector Machines). The experiments and practical usage show clearly that our system can provide a useful intuitive guide for biological researchers in their investigations and in the design of their experiments

Named entity recognition for Hungarian using various machine learning algorithms

In this paper we introduce a statistical Named Entity recognizer (NER) system for the Hungarian language. We examined three methods for identifying and disambiguating proper nouns (Artificial Neural Network, Support Vector Machine, C4.5 Decision Tree), their combinations and the effects of dimensionality reduction as well. We used a segment of Szeged Corpus [5] for training and validation purposes, which consists of short business news articles collected from MTI (Hungarian News Agency, www.mti.hu). Our results were presented at the Second Conference on Hungarian Computational Linguistics [7]. Our system makes use of both language dependent features (describing the orthography of proper nouns in Hungarian) and other, language independent information such as capitalization. Since we avoided the inclusion of large gazetteers of pre-classified entities, the system remains portable across languages without requiring any major modification, as long as the few specialized orthographical and syntactic characteristics are collected for a new target language. The best performing model achieved an F measure accuracy of 91.95%

CLASSIFIER COMBINATION IN SPEECH RECOGNITION

In statistical pattern recognition, the principal task is to classify abstract data sets. Instead of using robust but computational expensive algorithms it is possible to combine `weak´ classifiers that can be employed in solving complex classification tasks. In this comparative study, we will examine the effectiveness of the commonly used hybrid schemes - especially those used for speech recognition problems - concentrating on cases which employ different combinations of classifiers