Speaker normalization in noisy environments using subglottal resonances

2013 ◽  
Vol 134 (5) ◽  
pp. 4075-4075
Author(s):  
Harish Arsikere ◽  
Abeer Alwan
Keyword(s):  
Noisy Environments ◽  
Speaker Normalization ◽  
Subglottal Resonances

From Seeing and Reaching to Hearing and Speaking

2021 ◽  
pp. 404-479
Author(s):  
Stephen Grossberg
Keyword(s):  
Speech Production ◽  
Arm Movement ◽  
Noisy Environments ◽  
Multiple Sources ◽  
Speaker Normalization ◽  
Cocktail Party Problem ◽  
Item Order ◽  
Number Systems ◽  
Numerical Representations ◽  
Representations Of Space

This far-ranging chapter provides unified explanations of data about audition, speech, and language, and the general cognitive processes that they specialize. The ventral What stream and dorsal Where cortical stream in vision have analogous ventral sound-to-meaning and dorsal sound-to-action streams in audition. Circular reactions for learning to reach using vision are homologous to circular reactions for learning to speak using audition. VITE circuits control arm movement properties of synergy, synchrony, and speed. Volitional basal ganglia GO signals choose which limb to move and how fast it moves. VAM models use a circular reaction to calibrate VITE circuit signals. VITE is joined with the FLETE model to compensate for variable loads, unexpected perturbations, and obstacles. Properties of cells in cortical areas 4 and 5, spinal cord, and cerebellum are quantitatively simulated. Motor equivalent reaching using clamped joints or tools arises from circular reactions that learn representations of space around an actor. Homologous circuits model motor-equivalent speech production, including coarticulation. Stream-shroud resonances play the role for audition that surface-shroud resonances play in vision. They support auditory consciousness and speech production. Strip maps and spectral-pitch resonances cooperate to solve the cocktail party problem whereby humans track voices of speakers in noisy environments with multiple sources. Auditory streaming and speaker normalization use networks with similar designs. Item-Order-Rank working memories and Masking Field networks temporarily store sequences of events while categorizing them into list chunks. Analog numerical representations and place-value number systems emerge from phylogenetically earlier Where and What stream spatial and categorical processes.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Adaptive Speaker Normalization for CTC-Based Speech Recognition

2020 ◽  
Author(s):  
Fenglin Ding ◽  
Wu Guo ◽  
Bin Gu ◽  
Zhen-Hua Ling ◽  
Jun Du
Keyword(s):  
Speech Recognition ◽  
Speaker Normalization
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