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A study on the EFL students’ speech related anxiety in Taiwan  [cached]
Hsu, Tsu-Chia
International Journal of Research Studies in Language Learning , 2012,
Abstract: Global challenges in terms of producing more globally competitive graduates have given grounds to the need of students to enhance their English proficiencies. Although most Taiwanese students have studied English starting from their elementary education up to their senior high school years, however, research have shown that there is still a need to improve on the students’ English speech proficiency. This presentation details an empirical study which aims to investigate how Public Speaking Anxiety (PSA) affects English as Foreign Language (EFL) students who took a yearlong public speaking course in Taiwan. More specifically, the study seeks to answer these four major concerns: 1.) what are the underlying factors behind the students PSA; 2.) to what extent can gender differences affects PSA and time of preparation for a speech (TPS); 3.) to what extent can gender differences and the different type of audiences affect the reported levels of PSA; and 4.) what are the advantages/disadvantages brought about by the yearlong public speaking course. This case study adopts a mixed-method paradigm, wherein methodology from both quantitative and qualitative is systematically combined. Participants were 82 third-year technical-vocational college students. The Personal Report of Public Speaking Anxiety (PRPSA) quantitative survey was used to determine the level of students’ anxiety, while the qualitative focus group interview was accomplished to further understand the effects of PSA and gender differences with respects to TPS and types of audiences. Results show that the yearlong public speaking course had indeed helped diminish some if not all of the students’ PSA. Furthermore, relationship between PSA and gender differences of the audience was significant. Lastly, female students have longer TPS and higher reported PSA than male students, however, is caused mainly due to their being grade conscious and fear of performing badly in front of their classmates.
Speech Segregation based on Pitch Track Correction and Music-Speech Classification  [cached]
KIM, H.-G.,JANG, G.-J.,PARK, J.-S.,KIM, J.-H.
Advances in Electrical and Computer Engineering , 2012, DOI: 10.4316/aece.2012.02003
Abstract: A novel approach for pitch track correction and music-speech classification is proposed in order to improve the performance of the speech segregation system. The proposed pitch track correction method adjusts unreliable pitch estimates from adjacent reliable pitch streaks, in contrast to the previous approach using a single pitch streak which is the longest among the reliable pitch streaks in a sentence. The proposed music and speech classification method finds continuous pitch streaks of the mixture, and labels each streak as music-dominant or speech-dominant based on the observation that music pitch seldom changes in a short-time period whereas speech pitch fluctuates a lot. The speech segregation results for mixtures of speech and various competing sound sources demonstrated that the proposed methods are superior to the conventional method, especially for mixtures of speech and music signals.
Research on a Methodology to Model Speech Emotion
Y.U. Dong Mei,Y.U. Dong Mei
Journal of Engineering and Applied Sciences , 2012,
Abstract: Research was conducted to develop a methodology to model the emotional content of speech as a linear function of time and speech features. In this study, emotional type is coming from 6 base emotions (anger, disgust, fear, joy, sadness and surprise). But, in the application is not defined as a simple word, is quantified using the dimensions valence and arousal and the value of valance or arousal is expressed as some interval. Results demonstrate that the model is more excellence than others.
Continuous Bangla Speech Segmentation, Classification and Feature Extraction
Md. Mijanur Rahman,Md. Farukuzzaman Khan,Md. Al-Amin Bhuiyan
International Journal of Computer Science Issues , 2012,
Abstract: Continuous speech recognition is a multileveled pattern recognition task, which includes speech segmentation, classification, feature extraction and pattern recognition. In our work, a blind speech segmentation procedure was used to segment the continuously spoken Bangla sentences into words/sub-words like units using the end-point detection technique. These segmented words were classified according to the number of syllables and the sizes of the segmented words. MFCC signal analysis technique was used to extract the features of speech words, which including windowing. The developed system achieved the segmentation accuracy rate at about 98% and total 24 sub-classes of segmented words with MFCC features.
On the classification of q-algebras  [PDF]
Christian Fronsdal
Physics , 2000,
Abstract: The problem is the classification of the ideals of ``free differential algebras", or the associated quotient algebras, the q-algebras; being finitely generated, unital C-algebras with homogeneous relations and a q-differential structure. This family of algebras includes the quantum groups, or at least those that are based on simple (super) Lie or Kac-Moody algebras. Their classification would encompass the so far incompleted classification of quantized (super) Kac-Moody algebras and of the (super) Kac-Moody algebras themselves. These can be defined as singular limits of q-algebras, and it is evident that to deal with the q-algebras in their full generality is more rational than the examination of each singular limit separately. This is not just because quantization unifies algebras and superalgebras, but also because the points "q = 1" and "q = -1" are the most singular points in parameter space. In this paper one of two major hurdles in this classification program has been overcome. Fix a set of integers n_1,...,n_k, and consider the space B_Q of homogeneous polynomials of degree n_1 in the generator e_1, and so on. Assume that there are no constants among the polynomials of lower degree, in any one of the generators; in this case all constants in the space B_Q have been classified. The task that remains, the more formidable one, is to remove the stipulation that there are no constants of lower degree.
A probabilistic methodology for multilabel classification  [PDF]
Alfonso E. Romero,Luis M. de Campos
Computer Science , 2012,
Abstract: Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an exponential number of alternatives), a very common solution (the binary method) is frequently used, learning a binary classifier for every category, and combining them all afterwards. The assumption taken in this solution is not realistic, and in this work we give examples where the decisions for all the labels are not taken independently, and thus, a supervised approach should learn those existing relationships among categories to make a better classification. Therefore, we show here a generic methodology that can improve the results obtained by a set of independent probabilistic binary classifiers, by using a combination procedure with a classifier trained on the co-occurrences of the labels. We show an exhaustive experimentation in three different standard corpora of labeled documents (Reuters-21578, Ohsumed-23 and RCV1), which present noticeable improvements in all of them, when using our methodology, in three probabilistic base classifiers.
Classification of Speech for Clinical Data using Artificial Neural Network
C.R.Bharathi,V. Shanthi
International Journal of Computer Science Issues , 2011,
Abstract: A wide range of researches are carried out in speech signal processing for denoising, enhancement and more. Besides the other, stress management is important to improve disabled children speech. In order to provide proper speech practice for the disabled children, their speech is analyzed. Initially, the normal and pathological subjects speech are obtained with the same set of words. In this paper, classification of normal and pathological subjects speech is discussed. Initially Feature Extraction is implemented using well known Mel Frequency Cepstrum Coefficients (MFCC) for both words of normal and pathological subjects speech. Dimensionality reduction of features extracted is implemented using Principal Component Analysis (PCA). Finally the features are trained using Artificial Neural Network (ANN) for classification.
Classification Techniques used in Speech Recognition Applications: A Review
M.A.Anusuya,S.K.Katti
International Journal of Computer Technology and Applications , 2011,
Abstract: Classification phase is one of the most active research and application areas of speech recognition. The literature is vast and growing. This paper summarizes the some of the most important developments in the classification procedures of the speech recognition applications. The state of art of the classification technique has also been presented in this paper. Different classification techniques and their parameter estimation methods, properties, advantages, disadvantages along with their application areas are discussed with each classification method. Our purpose is to provide a synthesis of the published research in the area of speech recognition and stimulate further research interests and efforts in the identified topics. This paper presents an overview of several pattern classification methods available in literature for speech recognition applications.
Local Linear Wavelet Neural Network and RLS for Usable Speech Classification
Suchismita Sahoo,Sushree Sangita Sahoo,M R Senapati,P K Dash
International Journal of Computer Science Issues , 2011,
Abstract: While operating in a co-channel environment, the accuracy of the speech processing technique degrades. When more than one person is talking at same time, then there occurs the co-channel speech. The objective of usable speech segmentation is identification and extraction of those portions of co-channel speech that are degraded in a negligible range but still needed for various speech processing application like speaker identification. Some features like usable speech measures are extracted from the co-channel signal to differentiate between usable and unusable types of speech. The features are extracted recursively by this new method and variable length segmentation is carried out by making sequential decision on class assignment of LLWNN pattern classifier. The correct classification using this technique is 84.5% whereas the false classification is 15.5%. The result shows that the proposed classifier gives better classification and is robust.
Classification of Fricative Consonants for Speech Enhancement in Hearing Devices  [PDF]
Ying-Yee Kong, Ala Mullangi, Kostas Kokkinakis
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0095001
Abstract: Objective To investigate a set of acoustic features and classification methods for the classification of three groups of fricative consonants differing in place of articulation. Method A support vector machine (SVM) algorithm was used to classify the fricatives extracted from the TIMIT database in quiet and also in speech babble noise at various signal-to-noise ratios (SNRs). Spectral features including four spectral moments, peak, slope, Mel-frequency cepstral coefficients (MFCC), Gammatone filters outputs, and magnitudes of fast Fourier Transform (FFT) spectrum were used for the classification. The analysis frame was restricted to only 8 msec. In addition, commonly-used linear and nonlinear principal component analysis dimensionality reduction techniques that project a high-dimensional feature vector onto a lower dimensional space were examined. Results With 13 MFCC coefficients, 14 or 24 Gammatone filter outputs, classification performance was greater than or equal to 85% in quiet and at +10 dB SNR. Using 14 Gammatone filter outputs above 1 kHz, classification accuracy remained high (greater than 80%) for a wide range of SNRs from +20 to +5 dB SNR. Conclusions High levels of classification accuracy for fricative consonants in quiet and in noise could be achieved using only spectral features extracted from a short time window. Results of this work have a direct impact on the development of speech enhancement algorithms for hearing devices.
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