Open Access
Voiced/Unvoiced Decision for Speech Signals Based on Zero-Crossing Rate and Energy.
Rajesh Bachu,S. Kopparthi,B. Adapa,Buket D. Barkana +3 more
- pp 279-282
TLDR
This paper evaluated the results by dividing the speech sample into some segments and used the zero crossing rate and energy calculations to separate the voiced and unvoiced parts of speech and suggested that zero crossing rates are low for voiced part and high for unvoicing part.Abstract:
In speech analysis, the voiced-unvoiced decision is usually performed in extracting the information from the speech signals. In this paper, two methods are performed to separate the voiced and unvoiced parts of the speech signals. These are zero crossing rate (ZCR) and energy. In here, we evaluated the results by dividing the speech sample into some segments and used the zero crossing rate and energy calculations to separate the voiced and unvoiced parts of speech. The results suggest that zero crossing rates are low for voiced part and high for unvoiced part where as the energy is high for voiced part and low for unvoiced part. Therefore, these methods are proved effective in separation of voiced and unvoiced speech.read more
Citations
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Journal ArticleDOI
Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers
Mehmet Berkehan Akçay,Kaya Oguz +1 more
TL;DR: This work defines speech emotion recognition systems as a collection of methodologies that process and classify speech signals to detect the embedded emotions and identified and discussed distinct areas of SER.
Journal ArticleDOI
COVID-19 cough classification using machine learning and global smartphone recordings.
TL;DR: Although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the CO VID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98.
Proceedings ArticleDOI
Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals
TL;DR: Different methods of separating voiced and unvoiced segments of a speech signals are presented, based on short time energy calculation, short time magnitude calculation, and zero crossing rate calculation and on the basis of autocorrelation of different segments of speech signals to show that the voiced segment of speech remains periodic after applying autoc orrelation function.
Journal ArticleDOI
The LOCATA Challenge: Acoustic Source Localization and Tracking
Christine Evers,Heinrich W. Lollmann,Heinrich Mellmann,Alexander Schmidt,Hendrik Barfuss,Patrick A. Naylor,Walter Kellermann +6 more
TL;DR: The LOCAlization and Tracking Challenge (LOCATA) as discussed by the authors is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking.
Journal ArticleDOI
Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition
Abhay Upadhyay,Ram Bilas Pachori +1 more
TL;DR: Experimental results at various signal to noise ratios (SNRs) are included in order to show the effectiveness of the proposed method compared to the other existing methods for V/NV detection in speech signals.
References
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Book
Digital Processing of Speech Signals
TL;DR: This paper presents a meta-modelling framework for digital Speech Processing for Man-Machine Communication by Voice that automates the very labor-intensive and therefore time-heavy and expensive process of encoding and decoding speech.
Book
Discrete-Time Speech Signal Processing: Principles and Practice
TL;DR: This chapter discusses the Discrete-Time Speech Signal Processing Framework, a model based on the FBS Method, and its applications in Speech Communication Pathway and Homomorphic Signal Processing.
Journal ArticleDOI
A pattern recognition approach to voiced-unvoiced-silence classification with applications to speech recognition
TL;DR: A pattern recognition approach for deciding whether a given segment of a speech signal should be classified as voiced speech, unvoiced speech, or silence, based on measurements made on the signal, which has been found to provide reliable classification with speech segments as short as 10 ms.
Journal ArticleDOI
Cepstrum-based pitch detection using a new statistical V/UV classification algorithm
Sassan Ahmadi,Andreas Spanias +1 more
TL;DR: An improved cepstrum-based voicing detection and pitch determination algorithm is presented and is shown to be robust to additive noise and performance analysis on a large database indicates considerable improvement relative to the conventional cepStrum method.
Journal ArticleDOI
Voiced-unvoiced-silence classifications of speech using hybrid features and a network classifier
Yingyong Qi,B.R. Hunt +1 more
TL;DR: Voiced-unvoiced-silence classification of speech was done using a multilayer feedforward network and results indicated that the network performance was not significantly affected by the size of the training set and a classification rate as high as 96%.