On Features Obtained by Insertion of White Noise into Intermittently Removed Intervals of Speech Signals

1996 ◽  
Vol 8 (2) ◽  
pp. 144-148
Author(s):  
Manabu Ishihara ◽  
◽  
Jun Shirataki ◽  
Keyword(s):  
White Noise ◽  
Speech Signal ◽  
Sentence Comprehension ◽  
Digital Circuit ◽  
Speech Signals ◽  
The Relationship

In this study, a signal was synthesized by removing a speech signal at a certain uniform interval and inserting noise into those signal–absent parts. An auditory experiment was conducted to make clear how humans can hear such synthesized signals. In other words, the relationship between the size of noise and the intensity of signal sound and the relationship between the size of noise and clearness degree were made clear. On the basis of the result of the experiment, in case the size of the white noise inserted is smaller than OdB, a degree of sentence comprehension of over 90 percent is obtained as long as the removed intervals amount to around 60 to 50 percent. In this case, the degree of sentence comprehension is seen to have improved by over 30 percent, in view of the fact that the single syllable comprehension is around 50 to 60 percent. Starting with the region where the removed intervals exceed 50 percent, the degree of sentence comprehension goes down sharply, but this is considered to be due to an effect of the insertion of the white noise. On the basis of the results of this experiment, one of the auditory characteristics to be realized by a digital circuit was made clear.

scholarly journals Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

2021 ◽  
pp. 1-15
Author(s):  
Poovarasan Selvaraj ◽  
E. Chandra
Keyword(s):  
Real Time ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Time Delay Estimation ◽  
Variational Model ◽  
Speech Signals ◽  
Frequency Noise ◽  
Intrinsic Mode Functions ◽  
Real Time Applications

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

Grammaticality Judgments and Sentence Comprehension in Agrammatic Aphasia

1988 ◽  
Vol 31 (1) ◽  
pp. 72-81 ◽  
Author(s):  
Beverly B. Wulfeck
Keyword(s):  
Sentence Comprehension ◽  
Syntactic Processing ◽  
Semantic Cues ◽  
Grammaticality Judgment ◽  
Grammaticality Judgments ◽  
Agrammatic Aphasia ◽  
Healthy Control ◽  
Performance Domains ◽  
The Relationship ◽  
Neurologically Intact

The relationship between sentence comprehension and grammaticality judgment was examined for both neurologically intact and agrammatic aphasic subjects. Aphasic subjects were able to make grammaticality judgments and comprehension judgments, but were less accurate than healthy control subjects. However, the tasks appeared dissociated for the aphasic subjects: Both the effects of semantic cues and the hierarchy of difficulty of sentence types differed across the two tasks. Further, the findings suggest that not all aspects of morpho-syntactic processing may be equally disrupted in aphasia. The results argue against both a central deficit view of agrammatic aphasia, and a view suggesting that syntactic processing is intact whereas semantic or thematic mapping is not. Instead, the results indicate that the respective performance domains of comprehension and grammaticality judgment may draw on different processes and/or operate on different aspects of the language input.

scholarly journals The relationship between sentence comprehension and lexical-semantic retuning

2021 ◽  
Vol 116 ◽  
pp. 104188
Author(s):  
Rebecca A. Gilbert ◽  
Matthew H. Davis ◽  
M. Gareth Gaskell ◽  
Jennifer M. Rodd
Keyword(s):  
Sentence Comprehension ◽  
Lexical Semantic ◽  
The Relationship

RPSOVF Prediction Model for Speech Signal Series Based on UPSO

2019 ◽  
Vol 29 (06) ◽  
pp. 1950075
Author(s):  
Yumei Zhang ◽  
Xiangying Guo ◽  
Xia Wu ◽  
Suzhen Shi ◽  
Xiaojun Wu
Keyword(s):  
Prediction Model ◽  
Speech Signal ◽  
Signal Reconstruction ◽  
Volterra Model ◽  
Speech Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Nonlinear Prediction ◽  
Absolute Deviation ◽  
Low Performance

In this paper, we propose a nonlinear prediction model of speech signal series with an explicit structure. In order to overcome some intrinsic shortcomings, such as traps at the local minimum, improper selection of parameters, and slow convergence rate, which are always caused by improper parameters generated by, typically, the low performance of least mean square (LMS) in updating kernel coefficients of the Volterra model, a uniform searching particle swarm optimization (UPSO) algorithm to optimize the kernel coefficients of the Volterra model is proposed. The second-order Volterra filter (SOVF) speech prediction model based on UPSO is established by using English phonemes, words, and phrases. In order to reduce the complexity of the model, given a user-designed tolerance of errors, we extract the reduced parameter of SOVF (RPSOVF) for acceleration. The experimental results show that in the tasks of single-frame and multiframe speech signals, both UPSO-SOVF and UPSO-RPSOVF are better than LMS-SOVF and PSO-SOVF in terms of root mean square error (RMSE) and mean absolute deviation (MAD). UPSO-SOVF and UPSO-RPSOVF can better reflect trends and regularity of speech signals, which can fully meet the requirements of speech signal prediction. The proposed model presents a nonlinear analysis and valuable model structure for speech signal series, and can be further employed in speech signal reconstruction or compression coding.

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

A longitudinal study of idiom and text comprehension

2007 ◽  
Vol 34 (3) ◽  
pp. 473-494 ◽  
Author(s):  
M. CHIARA LEVORATO ◽  
MAJA ROCH ◽  
BARBARA NESI
Keyword(s):  
Text Comprehension ◽  
Sentence Comprehension ◽  
First Graders ◽  
Idiom Comprehension ◽  
Follow Up Study ◽  
Comprehension Skills ◽  
Elaboration Model ◽  
The Relationship ◽  
Different Levels

ABSTRACTThe relation between text and idiom comprehension in children with poor text comprehension skills was investigated longitudinally. In the first phase of the study, six-year-old first graders with different levels of text comprehension were compared in an idiom and sentence comprehension task. Text comprehension was shown to be more closely related to idiom comprehension than sentence comprehension. The follow-up study, carried out eight months later on less-skilled text comprehenders, investigated whether an improvement in text comprehension was paralleled by an improvement in idiom comprehension. The development of sentence comprehension was also taken into account. Children who improved in text comprehension also improved in idiom comprehension; this improvement was, instead, weakly related to an improvement in sentence comprehension. The relationship between text and idiom comprehension is discussed in the light of the Global Elaboration Model (Levorato & Cacciari, 1995).

Analysis of a Signal Transmission in a Pair of Izhikevich Coupled Neurons

2020 ◽  
Vol 15 (04) ◽  
pp. 195-206
Author(s):  
David. H. Margarit ◽  
Marcela V. Reale ◽  
Ariel F. Scagliotti
Keyword(s):  
White Noise ◽  
Resting State ◽  
Signal Transmission ◽  
Electrical Potential ◽  
Signal Amplitude ◽  
Receptor Neuron ◽  
Neuron Models ◽  
The Relationship ◽  
The Brain ◽  
Izhikevich Model

Individual neuron models give a comprehensive explanation of the behavior of the electrical potential of cell membranes. These models were and are a source of constant analysis to understand the functioning of, mainly, the complexity of the brain. In this work, using the Izhikevich model, we propose, analyze and characterize the transmission of a signal between two neurons unidirectionally coupled. Two possible states were characterized (sub-threshold and over-threshold) depending on the values of the signal amplitude, as well also the relationship between the transmitted and received signal taking into account the coupling. Furthermore, the activation of the emitting neuron (its transition from a resting state to spiking state) and the transmission to the receptor neuron were analyzed by adding white noise to the system.

Sign in / Sign up

Export Citation Format

Share Document