scholarly journals EVALUATION AND COMPARISON USING ACTIVITY SIGNALS OF SPEECH METHODS IN RIVER PLATE SPANISH USING BEPPA CORPUS

2016 ◽  
Vol 28 (1) ◽  
pp. 138-144
Author(s):  
Horderlin Vrangel Robles ◽  
Valentin Molina ◽  
Luis Martinez ◽  
Hermann Davila

The results obtained after comparing several algorithms which use basic methods of signal processing for speech activity detection of voice or VAD (Voice Activity Detection-VAD), were assessed in order to determine their effectiveness. The algorithms presented in this article are short-time or spectral energy based endpoint detection algorithm, the zero crossing rate method, and the higher order differential (High Order Difference, HOD) method. First, an introduction of the concept of VAD is presented and the need to apply such language algorithms in River Plate is Spanish. Then a summary of the state of the art techniques and algorithms for detecting voice activity is shown with evidence and experiments used to implement algorithms with BEPPA corpus (Evaluation Battery for Patients with Auditive Prostheses, BEPPA – in Spanish).

2019 ◽  
Vol 11 (2) ◽  
pp. 47-62 ◽  
Author(s):  
Xinchao Huang ◽  
Zihan Liu ◽  
Wei Lu ◽  
Hongmei Liu ◽  
Shijun Xiang

Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted list of audio segments. In the end, the article merely compares one segment with some adjacent segments in the sorted list so that the time complexity is decreased. After comparisons with other state of the art methods, the results show that the proposed method can identify the authentication of the input audio and locate the forged position fast and effectively.


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