spectral coefficient
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In the process of verifying Al-Quran memorization, a person is usually asked to recite a verse without looking at the text. This process is generally done together with a partner to verify the reading. This paper proposes a model using Siamese LSTM Network to help users check their Al-Quran memorization alone. Siamese LSTM network will verify the recitation by matching the input with existing data for a read verse. This study evaluates two Siamese LSTM architectures, the Manhattan LSTM and the Siamese-Classifier. The Manhattan LSTM outputs a single numerical value that represents the similarity, while the Siamese-Classifier uses a binary classification approach. In this study, we compare Mel-Frequency Cepstral Coefficient (MFCC), Mel-Frequency Spectral Coefficient (MFSC), and delta features against model performance. We use the public dataset from Every Ayah website and provide the usage information for future comparison. Our best model, using MFCC with delta and Manhattan LSTM, produces an F1-score of 77.35%

A longstanding hypothesis about the sensorimotor mechanisms underlying stuttering suggests that stuttered speech dysfluencies result from a lack of coarticulation. Formant-based measures of either the stuttered or fluent speech of children and adults who stutter have generally failed to obtain compelling evidence in support of the hypothesis that these individuals differ in the timing or degree of coarticulation. Here, we used a sensitive acoustic technique–spectral coefficient analyses–that allowed us to compare stuttering and nonstuttering speakers with regard to vowel-dependent anticipatory influences as early as the onset burst of a preceding voiceless stop consonant. Eight adults who stutter and eight matched adults who do not stutter produced C1VC2 words, and the first four spectral coefficients were calculated for one analysis window centered on the burst of C1 and two subsequent windows covering the beginning of the aspiration phase. Findings confirmed that the combined use of four spectral coefficients is an effective method for detecting the anticipatory influence of a vowel on the initial burst of a preceding voiceless stop consonant. However, the observed patterns of anticipatory coarticulation showed no statistically significant differences, or trends toward such differences, between the stuttering and nonstuttering groups. Combining the present results for fluent speech in one given phonetic context with prior findings from both stuttered and fluent speech in a variety of other contexts, we conclude that there is currently no support for the hypothesis that the fluent speech of individuals who stutter is characterized by limited coarticulation.

The use of classification techniques for machine health monitoring and fault diagnosis has been popular in recent years. System response in the form of time series data can be used to identify the type of defect and severity of defect. However, a central issue with time series classification is that of identifying appropriate features for classification. In this paper, we explore a new feature set based on delay differential equations (DDEs). DDEs have been used recently for extracting features for classification but have never been used to classify system responses. The Duffing oscillator, Van der Pol–Duffing (VDP-D) oscillator, Lu oscillator, and Chen oscillator are used as examples for dynamic systems, and the responses are classified into self-similar groups. Responses with the same period should belong to the same group. Misclassification rate is used as an indicator of the efficacy of the feature set. The proposed feature set is compared to a statistical feature set, a power spectral coefficient feature set, and a wavelet coefficient feature set. In the work described in this paper, a density-estimation algorithm called DBSCAN is used as the classification algorithm. The proposed DDE-based feature set is found to be significantly better than the other feature sets for classifying responses generated by the Duffing, Lu, and Chen systems. The wavelet and power spectral coefficient data sets are not found to be significantly better than the statistical feature set for these systems. None of the feature sets tested is discerning enough on the VDP-D system.

The use of classification techniques for machine health monitoring and fault diagnosis has been popular in recent years. System response in form of time series data can be used to identify type of defect, severity of defect etc. However, a central issue with time series classification is that of identifying appropriate features for classification. In this paper, we explore a new feature set based on a delay differential equations (DDEs). DDEs have been used recently for extracting features for classification but have never been used to classify system responses. The Duffing oscillator and Van der Pol–Duffing (VDP-D) oscillator are used as dynamic systems, and the responses are classified into self-similar groups. Responses with the same period should belong to the same group. Misclassification rate is used as an indicator of the efficacy of the feature set. The proposed feature set is compared to a statistical feature set, a power spectral coefficient feature set and a wavelet coefficient feature set. In work described in this paper, a density estimation algorithm called DBSCAN is used as the classification algorithm. The proposed DDE-based feature set is found to be significantly better than the other feature sets for the classifying responses generated by the Duffing system. The wavelet and the power spectral coefficient data sets are not found to be significantly better than the statistical feature set for the Duffing system. None of the feature sets tested are discerning enough on the VDP-D system.