Phonemic Networks Research Articles

Multimedia Network English Reading Teaching Model Based on Speech Recognition Confidence Learning Algorithm

With deepening internationalization, English has become an increasingly important communication tool. Because traditional English teaching has short teacher-student interaction time, lack of oral English training environment, and single learning method, the oral English teaching is not ideal, and the students’ “speaking” confidence is insufficient. Aimed at addressing the exposed problems of traditional English reading teaching, this paper proposes a multimedia-based English reading teaching mode. On this basis, establish a voice recognition phoneme network grid to detect the recognition results. Secondly, the lattice is used to generate the confusion network mesh, and the acoustic posterior probability is calculated. Then, the feature vector is input into the SVM classifier for confidence mark, and finally the feature is extracted by principal component analysis. The research shows that multimedia network teaching can teach more vividly, increasing the initiative of students. At the same time, it is shown that the speech recognition confidence learning algorithm can improve the language learning system.

Key nodes mining of root network and phoneme network of modern Chinese characters

Roots and phonemes are two important ingredients (but not independent ones) when learning the Chinese language. In order to optimize the learning strategy, various key nodes mining algorithms are implemented both in the root network (RN) and phoneme network (PN) of modern Chinese characters. The lists of important roots and phonemes, and eventually the important characters are dug out and suggested. Rather than the commonly used frequency dictionary of characters, one would find that results from the key nodes mining are more systematic. Moreover, roots and phonemes are studied from perspective of the multi-layer network, in the interest of exploring the correlation between them, which are also very useful when learning the Chinese language.

Constant Communities in Complex Networks

Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.

Statistical properties of Chinese phonemic networks

The study of properties of speech sound systems is of great significance in understanding the human cognitive mechanism and the working principles of speech sound systems. Some properties of speech sound systems, such as the listener-oriented feature and the talker-oriented feature, have been unveiled with the statistical study of phonemes in human languages and the research of the interrelations between human articulatory gestures and the corresponding acoustic parameters. With all the phonemes of speech sound systems treated as a coherent whole, our research, which focuses on the dynamic properties of speech sound systems in operation, investigates some statistical parameters of Chinese phoneme networks based on real text and dictionaries. The findings are as follows: phonemic networks have high connectivity degrees and short average distances; the degrees obey normal distribution and the weighted degrees obey power law distribution; vowels enjoy higher priority than consonants in the actual operation of speech sound systems; the phonemic networks have high robustness against targeted attacks and random errors. In addition, for investigating the structural properties of a speech sound system, a statistical study of dictionaries is conducted, which shows the higher frequency of shorter words and syllables and the tendency that the longer a word is, the shorter the syllables composing it are. From these structural properties and dynamic properties one can derive the following conclusion: the static structure of a speech sound system tends to promote communication efficiency and save articulation effort while the dynamic operation of this system gives preference to reliable transmission and easy recognition. In short, a speech sound system is an effective, efficient and reliable communication system optimized in many aspects.

Adaptation to a speaker's voice in a speech recognition system based on synthetic phoneme references

A speech recognition system based on synthetic generation of reference prototypes is described. The vocabulary and grammar are described in a finite-state phoneme network. In the transformation from symbolic to spectral representation, reduction rules modify the initial phoneme target values and a coarticulation module inserts interpolated transition states at phoneme boundaries. The phoneme templates are specified in terms of control parameters to a seriel formant synthesiser. At each state, a 16-channel filter bank section is computed from the synthesis parameters. The recognition process uses a time-synchronous dynamic programming technique to find the path in the network that minimises the accumulated spectral distance to the input utterance. A technique for dynamic adaptation to the speaker's voice source spectrum is performed during recognition. Without adaptation, the average recognition for ten male speakers was 88% on an isolated-word task using a 26-word vocabulary. Adding voice source adaptation raised the performance to 96%. On a vocabulary of 3 connected digits, the adaptation technique improved the recognition rate for six male speakers from 87.7% to 92.8%. The improvement was largest for subjects with low initial recognition rate, indicating the benefit of the voice source adaptation technique for certain voices. Changing the voice source model and optimising the adaptation time constant raised the recognition rate further to 96.1%. Current work is directed towards speaker adaptation of phoneme parameters and modelling of the variability of the parameter dynamics at phoneme boundaries.