Speech recognition of different sampling rates using fractal code descriptor

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.