ارزیابی یک ابزار تشخیص الگوی متوالی فازی (ماشین تطبیق الاستیک فازی) و کاربرد آن در تشخیص گفتار و دست خط Evaluation of a novel fuzzy sequential pattern recognition tool (fuzzy elastic matching machine) and its applications in speech and handwriting recognition

1. Introduction Sequential data often emerge in the measurement of time series, e.g. the acoustic features at the time frames of speech signals [1] and kinematic and biological signals in human movements [2]. Hence, sequential pattern recognition is increasingly used in many data analysis fields such as handwriting recognition [3,4], speech recognition [5,6], activity recognition [7,8], and gesture recognition [2]. In this approach, sequences of information are utilized in decision-making [9]. Sequential pattern recognition in time-series information matches the input data with a pattern, both of which being sequences of information. However, inequality in the length of sequences between the input data and the pattern is a typical problem. Studies in 1960s showed that an appropriate pattern recognition tool should be developed to overcome this problem in speech recognition [10]. One of the main solutions to this problem is the “time-wrapping” idea, presented by Vintsyk [11]. He showed how dynamic programming can be used to find the best assignment between two sequences. In 1970s and 1980s, many researchers proposed various models that mostly included modeling of acoustic information in a network with some finite states, deriving the stochastic information of acoustic features and comparing them with the input signal using the dynamic time-wrapping method [12]. Some examples of these works can be seen in [13–16]. The most well-known method in this field is Hidden Markov Model (HMM) [17]. The capabilities of the HMM such as the ability to match with stochastic sequential time series and suitable training algorithms have made it a pervasive tool in speech recognition. Since mid-1990s, the HMM has been a dominant method in handwriting and speech recognition [18]. Besides, many researchers have demonstrated its superiority to short time classifiers such as MLP [19] and SVM [8]. Despite the powerful mathematical modeling of the HMMs, the main criticisms about them fall into two categories: 1) limitations due to the selected mathematical modeling assumptions.