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This research aims to find out the correlation between English phoneme identification skills and word processing. It applies the quantitative approach with correlation design. The participants are 100 of 3rd- semester students in English Language Education Program. The correlational result reveals that it has correlation with .382 degrees in phoneme identification skill toward blending skill with the significance level .000, and .359 degrees in phoneme identification skill toward segmentation skill with the significance level .000. Then, the correlation result of English phoneme identification skill toward word processing is .462 degree with its significance .000. By the result, awareness to identify phoneme by initial, medial, and final sound correlates to the blending and segmenting skills which influence the comprehension of word. The more the students are able to identify phoneme based on its sound, the more the students will be able to blend and segment phoneme. Lastly, the ability to identify English phonemes is proven to be a skill that supports EFL learners on their productive and receptive skills. Then being able to identify its phonemes will assist on recognizing and processing English words appropriately so that English language teaching can be associated with the use of phoneme-based instruction on its teaching process. Highlights : • Ability to identify English phonemes is proven to be a skill that supports EFL learners on their English productive and receptive skills. • English phoneme identification skill contributes to blending and segmentation skill since phonemic awareness provides both decoding and encoding skill.

A newly developed factor analysis, origin-shifted factor analysis, was compared with a normal factor analysis to analyze the spectral changes of English speech. Our first aim was to investigate whether these analyses would cause differences in the factor loadings and the extracted spectral-factor scores. The methods mainly differed in whether to use cepstral liftering and an origin shift. The results showed that three spectral factors were obtained in four main frequency bands, but neither the cepstral liftering nor the origin shift distorted the essential characteristics of the factors. This confirms that the origin-shifted factor analysis is more recommendable for future speech analyses, since it would reduce the generation of noise in resynthesized speech. Our second aim was to further identify acoustic correlates of English phonemes. Our data show for the first time that the distribution of obstruents in English speech constitutes an L-shape related to two spectral factors on the three-dimensional configuration. One factor had center loadings around 4100 Hz, while the other was bimodal with peaks around 300 Hz and 2300 Hz. This new finding validates the use of multivariate analyses to connect English phonology and speech acoustics. [ABSTRACT FROM AUTHOR]

This paper presents wavelet-based power normalized spectrum for computing robust cepstral features named WP-PNCC features. The proposed technique computes wavelet packet-based short-time spectrum of speech signal. A nonlinear function is defined as relating power spectrum of clean speech to the power spectrum of speech corrupted with noise. The constants of function are computed from longer-duration speech spectrum, and the short-time spectrum for each frame is weighted with the power function. The weighted speech spectrum is processed with logarithmic and discrete cosine transform operation to compute cepstral coefficients. The cepstral coefficients thus obtained are processed with quantile-based cepstral dynamics normalization technique. The proposed features are examined with hidden Markov model classifier on TIFR database for Hindi phoneme classification task and on TIMIT database for English phoneme classification task along with mel-frequency cepstral coefficients, power normalized cepstral coefficients and 24-band wavelet-based features in clean and noisy environments. Different noises from NOISEX-92 database are used for preparing noisy database with SNR ranging from 20 dB to 0 dB. The results show enhanced performance of proposed features in all the considered cases. The simulations are performed on MATLAB 2015b. The performance of proposed features is also evaluated on hidden Markov model toolkit-based speech recognition system. The comparative results confirm the robustness of proposed features with sufficient improvement over other features examined in this paper. [ABSTRACT FROM AUTHOR]

A newly developed factor analysis, origin-shifted factor analysis, was compared with a normal factor analysis to analyze the spectral changes of English speech. Our first aim was to investigate whether these analyses would cause differences in the factor loadings and the extracted spectral-factor scores. The methods mainly differed in whether to use cepstral liftering and an origin shift. The results showed that three spectral factors were obtained in four main frequency bands, but neither the cepstral liftering nor the origin shift distorted the essential characteristics of the factors. This confirms that the origin-shifted factor analysis is more recommendable for future speech analyses, since it would reduce the generation of noise in resynthesized speech. Our second aim was to further identify acoustic correlates of English phonemes. Our data show for the first time that the distribution of obstruents in English speech constitutes an L-shape related to two spectral factors on the three-dimensional configuration. One factor had center loadings around 4100 Hz, while the other was bimodal with peaks around 300 Hz and 2300 Hz. This new finding validates the use of multivariate analyses to connect English phonology and speech acoustics.