Research on Speech Recognition Method in Multi Layer Perceptual Network Environment

Aiming at the problem of low recognition rate in speech recognition methods, a speech recognition method in multi-layer perceptual network environment is proposed. In the multi-layer perceptual network environment, the speech signal is processed in the filter by using the transfer function of the filter. According to the framing process, the speech signal is windowed and framing processed to remove the silence segment of the speech signal. At the same time, the average energy of the speech signal is calculated and the zero crossing rate is calculated to extract the characteristics of the speech signal. By analyzing the principle of speech signal recognition, the process of speech recognition is designed, and the speech recognition in multi-layer perceptual network environment is realized. The experimental results show that the speech recognition method designed in this paper has good speech recognition performance

Optimization of K Parameters on KNN in Gamelan Jegog Title Classification Using Time Domain Features

Bali is one of the provinces in Indonesia which has a lot of culture and arts, one of which is the Gamelan Jegog Bali. The technology nowadays can make it easier for humans to search for the title of a song that was previously unknown. This technology can be applied to the unknown title of Gamelan Jegog. The features used in this system are Short Time Energy and Zero Crossing Rate. The feature is extracted from Gamelan Jegog and then used to find the best k parameter from the K-Nearest Neighbor classifier. The results showed that the highest accuracy was 45% when the k parameter is 9. The amount of data used and the classification method used has an effect on the accuracy of this system when compared to similar studies.

Objective Evaluation of Therapeutic Effects of ADHD Medication by Analyzing Movements Using a Smart Chair with Piezoelectric Material

Attention-deficit hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. Several methods are available to evaluate ADHD therapeutic effects, including the Swanson, Nolan, and Pelham (SNAP) questionnaire, the Vanderbilt ADHD Diagnostic Rating Scale, and the visual analog scale. However, these scales are subjective. In this study, a piezoelectric material was applied to a medical chair to objectively evaluate the therapeutic effect of ADHD medication before and after treatment. A total of 22 patients (18 boys and 4 girls) with ADHD were enrolled. During the appointment, the patients’ movements were recorded by the piezoelectric material before being analyzed. The variance, zero-crossing rate, and high energy rate of movements were used to analyze the signal in this study. The results showed the variance, zero-crossing rate, and high energy rate in patients with ADHD all decreased significantly after 1 month of methylphenidate use. Although the hyperactivity subscales of SNAP obtained from parents and teachers demonstrated significant decreases after 1 month of medication, the reduction rate of the three aforementioned measurements decreased more than hyperactivity subscales. This suggests that the use of a smart chair equipped with a piezoelectric material is an objective and useful method for evaluating the therapeutic effects of ADHD medication.

Stop gap removal using spectral parameters for stuttered speech signal

Stuttering is an involuntary disturbance in the fluent flow of speech characterized by disfluencies such as stop gaps, sound or syllable repetition or prolongation. There are high proportion of stop gaps in stuttering. This work presents automatic removal of stop gaps using combination of spectral parameters such as spectral energy, centroid, Entropy and Zero crossing rate. A method for detecting and removing stop gaps based on threshold is discussed in this paper

A Proposed Speaker Recognition Method B Based on Long-Term Voice Features and Fuzzy Logic

Speaker recognition depends on specific predefined steps. The most important steps are feature extraction and features matching. In addition, the category of the speaker voice features has an impact on the recognition process. The proposed speaker recognition makes use of biometric (voice) attributes to recognize the identity of the speaker. The long-term features were used such that maximum frequency, pitch and zero crossing rate (ZCR). In features matching step, the fuzzy inner product was used between feature vectors to compute the matching value between a claimed speaker voice utterance and test voice utterances. The experiments implemented using (ELSDSR) data set. These experiments showed that the recognition accuracy is 100% when using text dependent speaker recognition.

Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate

Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs.

Speech file compression by eliminating unvoiced/silence components

The main objective of this study is to have noise component of a speech signal eliminated and compressing it by storing the locations and durations of silence regions. The separation between voiced, unvoiced, and silence regions are done by using the Short Time Energy (STE) and Zero Crossing Rate (ZCR) methodologies. All operations in this study have been performed by using the User Interface (UI) developed on MATLAB®. These operations include voice recording, playing the recording, eliminating the unwanted regions, playing the modified recording, saving of original and compressed files and loading the recording compressed.