Effects of User Controlled Speech Rate on Intelligibility in Noisy Environments

2018 ◽  
Author(s):  
John S. Novak ◽  
Robert V. Kenyon
Keyword(s):  
Speech Rate ◽  
Noisy Environments

scholarly journals Development of speech technologies to support hearing through mobile terminal users

2015 ◽  
Vol 4 ◽  
Author(s):  
Taro Togawa ◽  
Takeshi Otani ◽  
Kaori Suzuki ◽  
Tomohiko Taniguchi
Keyword(s):  
Speech Rate ◽  
Voice Quality ◽  
Mobile Terminal ◽  
The Other ◽  
Communication Tool ◽  
Noisy Environments ◽  
Mobile Terminals ◽  
Enhancement Technology ◽  
Rate Conversion ◽  
The Voice

Mobile terminals have become the most familiar communication tool we use, and various types of people have come to use mobile terminals in various environments. Accordingly, situations in which we talk over the telephone in noisy environments or with someone who speaks fast have increased. However, it is sometimes difficult to hear a person's voice in these cases. To make the voice received through mobile terminals easy to hear, authors have developed two technologies. One is a voice enhancement technology that emphasizes a caller's voice according to the noise surrounding the recipient, and the other is a speech rate conversion technology that slows speech while maintaining voice quality. In this paper, we explain the trends and the features of these technologies and discuss ways to implement their algorithms on mobile terminals.

The Successful Stuttering Management Program: A Preliminary Report on Outcomes

2010 ◽  
Vol 20 (1) ◽  
pp. 20-25 ◽  
Author(s):  
Jim Tsiamtsiouris ◽  
Kim Krieger
Keyword(s):  
Preliminary Report ◽  
Speech Rate ◽  
Management Program ◽  
Intensive Program ◽  
Adults Who Stutter

Abstract The purpose of this study was to test the hypothesis that adults who stutter will exhibit significant improvements after attending a residential, 3-week intensive program that focuses on avoidance reduction and stuttering modification therapy. Preliminary analyses focused on four measures: (a) SSI-3, (b) speech rate, (c) S-24 Scale, and (d) OASES. Results indicated significant improvements on all of the measures.

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.

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