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Author: search.filters.author.Hokking, Rattaphon

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    A hybrid of fractal code descriptor and harmonic pattern generator for improving speech recognition of different sampling rates
    (2018-01-01)
    Hokking, Rattaphon
    ;
    Woraratpanya, Kuntpong
    Currently, the different sampling rate for speech recognition is a grand challenge due to supporting applications of divergent platform devices, such as mobile device interaction, interactive voice response system, voice search, voice dictation and voice identification. Furthermore, such applications require efficient speech features to represent input signals. However, the different sampling rates of speech signals lead to the different features. This phenomenon comes from speech harmonic signal lost. It becomes a key factor that decreases the speech recognition rate. Therefore, this paper proposes a hybrid of fractal code descriptor and harmonic pattern generator to convert all different sampling rate signals to standardized signals. In this method, an independent resolution property of fractal code descriptor is applied to training and testing speech signals. Then, the pitches of such signals are used to recover harmonic pattern of lost signals. This method can effectively reconstruct speech signals at any sampling rates. When its performance is evaluated with AN4 corpus of CMU Sphinx speech recognition engine, the experimental results show that the proposed method can significantly improve the speech recognition rate, even if the sampling rate of testing speeches differs from that of training speeches.
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    Speech recognition of different sampling rates using fractal code descriptor
    (2016-11-18)
    Hokking, Rattaphon
    ;
    Woraratpanya, Kuntpong
    ;
    Kuroki, Yoshimitsu
    Currently, the use of speech recognition is increaseingly in many applications such as mobile device interaction, interactive voice response system, voice search, voice dictation and voice identification. The heart of such applications is speech features needed to represent input signals. However, in real applications, speech signals are sampled with various sampling rates. The different sampling rates of input speech lead to the different features. This makes the speech recognition rate dropping. Therefore, this paper proposes an independent resolution descriptor based on fractal codes obtained by fractal encoding and decoding processes. The encoding process extracts fractal codes from partitioned speech signals, whereas the decoding process reconstructs independent resolution speech signals from the fractal codes. This method can effectively reconstruct speech signals at any sampling rates, especially at a higher sampling rate, which is a grand challenge. The proposed method is evaluated the performance by testing with AN4 corpus of CMU Sphinx speech recognition engine. The experimental results show that the proposed method can improve the accuracy of speech recognition, even if the sampling rate of testing speeches differs from that of training speeches.
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    Text-background decomposition for thai text localization and recognition in natural scenes
    (2014-01-12)
    Woraratpanya, Kuntpong
    ;
    Pasupa, Kitsuchart
    ;
    Suttapakti, Ungsumalee
    ;
    Boonchukusol, Pimlak
    ;
    Titijaroonroj, Taravichet
    Thai text localization and recognition in natural scenes is still a grand challenge in current applications. However, the efficiency of recognition rates depends on text localization, i.e., the higher purity of text-background decomposition leads to the higher accuracy rate of character recognition. In order to achieve this purpose, the text-background decomposition methods, namely adaptive boundary clustering (ABC) and n-point boundary clustering (n-PBC), are proposed to improve a precision of text localization. These methods are evaluated by self-en-tropy for purity measure. Based on 300 test images, the experimental results demonstrate that the ABC method achieves the very low self-entropy, i.e., the low self-entropy implies the good decomposition of text and background. Furthermore, based on 8,077 characters in natural scene test images, the ABC method helps increase the precision of text localization and improves the accuracy rate of character recognition, when compared to the conventional methods.