美声教学中强化意大利语语音语感训练方法探讨

意大利在美声唱法发展历史中占据着重要的地位，是当前国际上公认的美声唱法发源地，对美声唱法的发展和传承产生了重要的影响。当前世界上美声唱法的主要风格和技巧的形成都与意大利语的发音规律存在紧密的联系，所以在美声教学中，可以从意大利语语音语感训练入手，探索有效方法，为强化美声教学提供良好的支持。本文将意大利语语音语感训练辅助美声教学作为研究对象，希望提供相应的参照。

Speech emotion recognition using semi-supervised discriminant analysis

Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.

Emotion recognition of Uyghur speech using uncertain linear discriminant analysis

To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.

The Emotional Approach of Foreign Vowel Acquisition Within the Example of British English Vowels

The aim of the article is to present results of research that was performed with 97 Polish students of the second and third year of English Philology. The purpose of the research is to examine how conscious manipulation of facial expressions aids acquisition of foreign vowels by learners, regardless of their native language and the culture they have been brought up in. Taking advantage of achievements derived from such disciplines as psychology of emotions and phonetics depicted as a physical process, an attempt is made to find a tool that improves teaching/learning of foreign vowels, that is to say, an effort is put in search of a useful method to make the phonetic process faster and more accurate. Teachers of English are encouraged to put the method, which is described in detail in the paper, into practice with their own mother languages and to share opinions about the method with colleagues. Similarly, it is believed that it can be applied to courses of other languages than just English. Teachers of those languages are encouraged to try to use it, too.

Compression and reconstruction of speech signals based on compressed sensing

Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.