Adaptive method for SNR estimation in speech signal

1996 ◽  
Vol 32 (5) ◽  
pp. 421 ◽  
Author(s):  
J. Rodriguez ◽  
F. Rios ◽  
R. Escaño-Quero ◽  
J.F. Martin
Keyword(s):  
Speech Signal ◽  
Adaptive Method ◽  
Snr Estimation

A New 3D Discrete Hyperchaotic System and Its Application in Secure Transmission

2019 ◽  
Vol 29 (14) ◽  
pp. 1950206
Author(s):  
Wangshu Li ◽  
Wenhao Yan ◽  
Ruoxun Zhang ◽  
Chuanfu Wang ◽  
Qun Ding
Keyword(s):  
Lyapunov Exponent ◽  
Chaotic System ◽  
Speech Signal ◽  
Approximate Entropy ◽  
Adaptive Method ◽  
Transmission System ◽  
Hyperchaotic System ◽  
Initial Value ◽  
Response Method ◽  
Secure Transmission

In this paper, a new 3D discrete hyperchaotic system is constructed, and its Lyapunov exponent and approximate entropy are calculated. We adopt the drive-response method and self-adaptive method to make the hyperchaotic system to reach synchronization, and then design an encrypted transmission system based on the synchronization of this discrete hyperchaotic system. In the synchronous hyperchaotic system, the initial value related to the hash value of the speech signal of the chaotic system is designed. Some security analyses are studied in detail.

Improved CEEMDAN Based Speech Signal Analysis Algorithm for Mental Disorders Diagnostic System

2019 ◽  
Vol 10 (1) ◽  
pp. 22-47 ◽  
Author(s):  
Alan K. Alimuradov ◽  
Alexander Yu. Tychkov ◽  
Andrey V. Kuzmin ◽  
Pyotr P. Churakov ◽  
Alexey V. Ageykin ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Speech Signal ◽  
Block Diagram ◽  
Diagnostic System ◽  
Frequency Measurement ◽  
Adaptive Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Diagnostic Systems ◽  
Mode Decomposition ◽  
Pitch Frequency

An automated algorithm for pitch frequency measurement for diagnostic systems of borderline mental disorders is developed. It is based on decomposition of a speech signal into frequency components using an adaptive method for analyzing of non-stationary signals, improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN), and isolating the component containing pitch. A block diagram for the developed algorithm and a detailed mathematical description are presented. A research of the algorithm using the formed verified signal base of healthy patients, and male and female patients with psychogenic disorders, aged from 18 to 60, is conducted. The research results are evaluated in comparison with the known algorithms for pitch frequency measurement. In accordance with the results of the study, the developed algorithm for pitch frequency measurement provides an accuracy increase in determination of borderline mental disorders: for the error of the first kind, on the average, it is more accurate by 10.7%, and for the second type error by 4.7%.

Perceptual Learning of Vocoded Speech With and Without Contralateral Hearing: Implications for Cochlear Implant Rehabilitation

2020 ◽  
pp. 1-10
Author(s):  
Martin Chavant ◽  
Alexis Hervais-Adelman ◽  
Olivier Macherey
Keyword(s):  
Cochlear Implant ◽  
Perceptual Learning ◽  
Speech Signal ◽  
Training Phase ◽  
Monosyllabic Words ◽  
Low Pass ◽  
Contralateral Ear ◽  
Number Of Individuals ◽  
Insight Into ◽  
Vocoded Speech

Purpose An increasing number of individuals with residual or even normal contralateral hearing are being considered for cochlear implantation. It remains unknown whether the presence of contralateral hearing is beneficial or detrimental to their perceptual learning of cochlear implant (CI)–processed speech. The aim of this experiment was to provide a first insight into this question using acoustic simulations of CI processing. Method Sixty normal-hearing listeners took part in an auditory perceptual learning experiment. Each subject was randomly assigned to one of three groups of 20 referred to as NORMAL, LOWPASS, and NOTHING. The experiment consisted of two test phases separated by a training phase. In the test phases, all subjects were tested on recognition of monosyllabic words passed through a six-channel “PSHC” vocoder presented to a single ear. In the training phase, which consisted of listening to a 25-min audio book, all subjects were also presented with the same vocoded speech in one ear but the signal they received in their other ear differed across groups. The NORMAL group was presented with the unprocessed speech signal, the LOWPASS group with a low-pass filtered version of the speech signal, and the NOTHING group with no sound at all. Results The improvement in speech scores following training was significantly smaller for the NORMAL than for the LOWPASS and NOTHING groups. Conclusions This study suggests that the presentation of normal speech in the contralateral ear reduces or slows down perceptual learning of vocoded speech but that an unintelligible low-pass filtered contralateral signal does not have this effect. Potential implications for the rehabilitation of CI patients with partial or full contralateral hearing are discussed.

“I Can See What You’re Saying”: Clinical Utility of Spectral Moment Analysis

2011 ◽  
Vol 21 (2) ◽  
pp. 44-54
Author(s):  
Kerry Callahan Mandulak
Keyword(s):  
Speech Production ◽  
Speech Signal ◽  
Clinical Utility ◽  
Acoustic Analysis ◽  
Moment Analysis ◽  
Analysis Tool ◽  
Spectral Moment ◽  
Clinical Measure ◽  
Perceptual Analysis ◽  
Disordered Speech

Spectral moment analysis (SMA) is an acoustic analysis tool that shows promise for enhancing our understanding of normal and disordered speech production. It can augment auditory-perceptual analysis used to investigate differences across speakers and groups and can provide unique information regarding specific aspects of the speech signal. The purpose of this paper is to illustrate the utility of SMA as a clinical measure for both clinical speech production assessment and research applications documenting speech outcome measurements. Although acoustic analysis has become more readily available and accessible, clinicians need training with, and exposure to, acoustic analysis methods in order to integrate them into traditional methods used to assess speech production.

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

2020 ◽  
pp. 65-72
Author(s):  
V. V. Savchenko ◽  
A. V. Savchenko
Keyword(s):  
Speech Signal ◽  
Communication Channel ◽  
Speaker Identification ◽  
A Priori ◽  
Small Samples ◽  
Practical Implementation ◽  
Identification System ◽  
New Information ◽  
Discrimination Criterion ◽  
A Priori Uncertainty

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

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