نتایج جستجو برای: Phoneme Recognition
تعداد نتایج: 254307 فیلتر نتایج به سال:
Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...
Phoneme recognition is one of the fundamental phases of automatic speech recognition. Coarticulation which refers to the integration of sounds, is one of the important obstacles in phoneme recognition. In other words, each phone is influenced and changed by the characteristics of its neighbor phones, and coarticulation is responsible for most of these changes. The idea of modeling the effects o...
The present study aimed to investigate of reaction time in terms of phoneme recognition: A comparative study among Iranian Upper-Intermediate vs. Advanced EFL Learners at Institute level. The main question this study tried to answer was whether there is no difference in reaction time in terms of phoneme recognition in Iranian learners at Institute level. To answer the question, 5Upper-Intermedi...
in this paper, the efficiency of persian speech phonemes from the point of view of efficiency in speaker recognition has been studied, and then with due attention to efficiencies, the ranking of phonemes has been done. for estimating the efficiencies of phonemes, we have introduced one criterion that has been defined in the form of phonemes “inter speaker distance” to “intra speaker distance” r...
The design for new feature extraction methods out of the speech signal and combination of their obtained information is one of the most effective approaches to improve the performance of automatic speech recognition (ASR) system. Recent researches have been shown that the speech signal contains nonlinear and chaotic properties, but the effects of these properties are not used in the continuous ...
the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...
In this paper, we have analyzed the impact of confusions on the robustness of phoneme recognitions system. The confusions are detected at the pronunciation and the confusions matrices of the phoneme recognizer. The confusions show that some similarities between phonemes at the pronunciation affect significantly the recognition rates. This paper proposes to understand those confusions in order t...
Speech recognition consists of converting input sound into a sequence phonemes, then finding text for the using language models. Therefore, phoneme classification performance is critical factor successful implementation speech system. However, correctly distinguishing phonemes with similar characteristics still challenging problem even state-of-the-art methods, and errors are hard to be recover...
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