scholarly journals Stop gap removal using spectral parameters for stuttered speech signal

2021 ◽  
Vol 10 (3) ◽  
pp. 1862-1866
Keyword(s):  
Speech Signal ◽  
Spectral Energy ◽  
Spectral Parameters ◽  
Zero Crossing ◽  
Syllable Repetition ◽  
Zero Crossing Rate ◽  
Automatic Removal

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

scholarly journals Research on Speech Recognition Method in Multi Layer Perceptual Network Environment

2021 ◽  
Vol 15 ◽  
pp. 996-1004
Author(s):  
Kai Zhao ◽  
Dan Wang
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Average Energy ◽  
Signal Recognition ◽  
Network Environment ◽  
Recognition Method ◽  
Zero Crossing ◽  
Zero Crossing Rate

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

scholarly journals Zero crossing rate and Energy of the Speech Signal of Devanagari Script

2014 ◽  
Vol 4 (1) ◽  
pp. 01-05 ◽  
Author(s):  
D.S. Shete ◽  
◽  
Prof. S.B. Patil ◽  
Prof. S.B. Patil
Keyword(s):  
Speech Signal ◽  
Zero Crossing ◽  
Zero Crossing Rate

Voice Activity Detection with Decision Trees in Noisy Environments

2011 ◽  
Vol 128-129 ◽  
pp. 749-752 ◽  
Author(s):  
Da Li Hu ◽  
Liang Zhong Yi ◽  
Zheng Pei ◽  
Bing Luo
Keyword(s):  
Decision Trees ◽  
Speech Signal ◽  
Accurate Data ◽  
Activity Detection ◽  
Noisy Environments ◽  
Zero Crossing ◽  
Zero Crossing Rate ◽  
Simulation Results ◽  
Voice Activity ◽  
Different Parts

An improved project based on double thresholds method in noisy environments is proposed for robust endpoints detection. Firstly, in this method, the distribution of zero crossing rate (ZCR) on the preprocessed signal is taken into account, and then the speech signal is divided into different parts to obtain appropriate thresholds with decision trees on the basis of the ZCR distribution. Finally, the double thresholds method, focusing on different importance of the energy and ZCR, is taken in the corresponding situation to determine the input segment is speech or non-speech. Simulation results indicate that the proposed method with decision trees obtains more accurate data than the traditional double thresholds method.

scholarly journals Speech file compression by eliminating unvoiced/silence components

2021 ◽  
Vol 3 (1) ◽  
pp. 11-14
Author(s):  
Arda Şahin ◽  
Mehmet Zübeyir Ünlü
Keyword(s):  
User Interface ◽  
Speech Signal ◽  
Noise Component ◽  
Zero Crossing ◽  
Zero Crossing Rate ◽  
Short Time ◽  
Short Time Energy ◽  
Voice Recording

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.

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

2021 ◽  
Vol 39 (1B) ◽  
pp. 1-10
Author(s):  
Iman H. Hadi ◽  
Alia K. Abdul-Hassan
Keyword(s):  
Fuzzy Logic ◽  
Speaker Recognition ◽  
Recognition Accuracy ◽  
Inner Product ◽  
Maximum Frequency ◽  
Recognition Method ◽  
Data Set ◽  
Zero Crossing ◽  
Zero Crossing Rate

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.

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