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Speech based emotion recognition finds numerous applications in automated speech services such as interactive voice recognition systems. It has great implications in investigative application like lie detectors, in medical applications such as diagnosis of mental depression etc. This work aims at developing a feature based emotion recognition system. The speech recordings with the emotions-anger,...
Automatic voiceprint recognition, posited on human speech signal, serves many salient practical applications. A number of studies are undertaken on the basis of normal speech. This research intends to develop automatic voiceprint recognition system on the basis of emotion speech signal in Indonesia language. The study is limited to four different people with speeches of four distinctive emotional...
This paper presents a review on few notable speech recognition models that are reported in the last decade. Firstly, the models are categorized into sparse models, learning models and domain - specific models. Subsequently, the characteristics of the models have been observed using speech constraints, algorithmic constraints and performance constraints. The performance of these models reported in...
This paper suggested a technique based on MFCC analysis for audio signals with speech classification application. The proposed work used multi-resolution (wavelet) analysis and spectral analysis based features for feature extraction. The proposed approach uses a no. of features like Mel Frequency Cepstral Coefficient (MFCC), and FFT Coefficients combined with wavelet based features. In addition, accuracy...
This paper aimed at introducing a completely automated Arabic phone recognition system based on Enhanced Wavelet Packets Best Tree Encoding (EWPBTE) 15-point speech feature. The process of enhancing of WPBTE is provided by adding energy component to WPBTE, which is implemented in Matlab software and makes an enhancement of 65 % to recognizer accuracy which is the most contribution in this paper. EWPBTE...
Automatic recognition of emotions in speech has attracted the attention of the research community in recent years. Some of the most relevant proposed applications of it are in call-centers. In these scenarios the speech is distorted by compression algorithms. The effects of such distortion on the performance of systems for automatic recognition of emotions must be assessed. In this study these effects...
A new method of classification of a speaker’s gender based on cumulant coefficients is proposed. The effect of an additive noise and measurement error of classification signs on accuracy of classification is analyzed. The expediency of construction of an adaptive system of classification operating with considering of masking of a speech signal by noise is shown. Comparison of the proposed method of...
The paper deals with the problem of improving speech recognition by combining outputs of several different recognizers. We are presenting our results obtained by experimenting with different classification methods which are suitable to combine outputs of different speech recognizers. Methods which were evaluated are: k-Nearest neighbors (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant...
Even if the Vector Space Model used for document representation in information retrieval systems integrates a small quantity of knowledge it continues to be used due to its computational cost, speed execution and simplicity. We try to improve this document representation by adding some syntactic information such as the parts of speech. In this paper, we have evaluated three different tagging algorithms...
Parkinson's disease (PD) is a disorder of the central nervous system and about 89% of the people with PD suffering from speech and voice disorders. In this paper, we adopted a dynamic feature selection based on fuzzy entropy measures for speech pattern classification of Parkinson's diseases. To investigate the effect of feature selection, Linear Discriminant Analysis (LDA) was applied to distinguish...
This paper tackles the Romanian syllabification and stress assignment problems, and proposes an efficient machine learning based solution. We show that by designing the appropriate feature sets for each specific problem, learning algorithms achieve satisfactory accuracy rates for both problems (∼92% for syllabification, ∼85% for stress assignment), even for relatively small training set sizes. We...
Cry segmentation is an essential preprocessing step in any infant crying diagnosis system. Besides crying sounds consisting of expiration phases followed by short periods of inspiration episodes, each recording of newborn cries also includes silence sections as well as other sounds such as speech of caregivers, noise and sound of medical equipments. This paper is devoted to a newly developed Empirical...
Feature selection is a strategy that aims at making text classifiers more efficient and accurate. In this paper, we proposed a novel feature selection method based on Tibetan grammar for Tibetan classification. Tibetan language express grammatical meaning through the function words and word order, and the function word has large proportions. By analyzing the Tibetan grammar and distribution of part...
This paper deals with live subtitling of TV ice-hockey commentaries using automatic speech recognition technology. Two methods are presented - a direct transcription of a TV program and a re-speaking approach. Practical issues emerging from the real subtitling system are introduced and their solutions are proposed. Acoustic and language modelling is described as well as modifications of existing live...
Use of modern technological advances in real-time biomedical analysis is very crucial. Current work focuses on glottal pathology discrimination based on non-invasive speech analysis techniques. Primary set back in developing such method is irregular performance depreciation of several state of the art acoustic features. To excuse such problems, we have used glottal to noise excitation ratio, which...
Parts of speech tagging is an important research topic in Natural Language Processing research are. Since it is one among the first steps of any natural language processing (NLP) techniques such as machine translation, if any error happens for tagging the same will repeat in the whole NLP process. So far works had been done on POS tagging based on SVM, MBLP, HMM, Ngram. All of these methods were not...
Sub-word units like morphemes are selected as the lexicon for highly inflectional languages, as they can provide better coverage and a smaller vocabulary size. However, short units shrink the context of statistical models, prone to morpho-phonetic changes, and not always outperform the word based model. When sequence of units are merged or split, unit boundaries are phonetically harmonized in the...
The evaluation of cleft palate (CP) speech is a critical clinical treatment. The most typical characteristics of CP speech include hypernasality and consonant misarticulation. Currently, the evaluation of CP speech is carried out by experienced speech therapists. It strongly depends on their clinical experience and subjective judgment. This work aims to propose an automatic evaluation system of resonance...
In low resource Automatic Speech Recognition (ASR), one usually resorts to the Statistical Machine Translation (SMT) technique to learn transform rules to refine grapheme lexicon. To do this, we face two challenges. One is to generate grapheme sequences from the training data as the targets, which is paired with the original transcripts to train SMT models; the other is to effectively prune the learned...
In the current social, technological and economic context, customers make their decisions based mostly on the opinion of other consumers. On the other side, companies need quick feedback from their customers in order to adapt to their needs in real time. The effective connection between these two aspects relies on opinion mining tools, which automatically process consumers' reviews and opinions about...
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