Loading…
Segmentation of Speech and Humming in Vocal Input
Non-verbal vocal interaction (NVVI) is an interaction method in which sounds other than speech produced by a human are used, such as humming. NVVI complements traditional speech recognition systems with continuous control. In order to combine the two approaches (e.g. "volume up, mmm") it i...
Saved in:
Published in: | Radioengineering 2012-09, Vol.21 (3), p.923-929 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 929 |
container_issue | 3 |
container_start_page | 923 |
container_title | Radioengineering |
container_volume | 21 |
creator | A. J. Sporka O. Polacek J. Havlik |
description | Non-verbal vocal interaction (NVVI) is an interaction method in which sounds other than speech produced by a human are used, such as humming. NVVI complements traditional speech recognition systems with continuous control. In order to combine the two approaches (e.g. "volume up, mmm") it is necessary to perform a speech/NVVI segmentation of the input sound signal. This paper presents two novel methods of speech and humming segmentation. The first method is based on classification of MFCC and RMS parameters using a neural network (MFCC method), while the other method computes volume changes in the signal (IAC method). The two methods are compared using a corpus collected from 13 speakers. The results indicate that the MFCC method outperforms IAC in terms of accuracy, precision, and recall. |
format | article |
fullrecord | <record><control><sourceid>doaj</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_7313cfea69944f4eb231fa316484b779</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_7313cfea69944f4eb231fa316484b779</doaj_id><sourcerecordid>oai_doaj_org_article_7313cfea69944f4eb231fa316484b779</sourcerecordid><originalsourceid>FETCH-LOGICAL-d221t-1bb38733d5421ade9659df83bd2280721dbbd1fc48ff74edc391cdeec72eff923</originalsourceid><addsrcrecordid>eNotjc1Kw0AURmeh0Fr7DvMCgdx7J5nMUoraQMFF1W2Y3zglmQlJuvDtLerqg3PgfHdsCwhlgRXghj0sy6Usa8Cq3DI4-370adVrzInnwM-T9_aL6-T48TqOMfU8Jv6ZrR54m6br-sjugx4Wv__fHft4eX4_HIvT22t7eDoVDhHWAoyhRhK5SiBo51VdKRcaMjfdlBLBGeMgWNGEIIV3lhRYd_uW6ENQSDvW_nVd1pdumuOo5-8u69j9gjz3nZ7XaAffSQKywetaKSGC8AYJgiaoRSOMlIp-ANI0S7I</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Segmentation of Speech and Humming in Vocal Input</title><source>IngentaConnect Journals</source><creator>A. J. Sporka ; O. Polacek ; J. Havlik</creator><creatorcontrib>A. J. Sporka ; O. Polacek ; J. Havlik</creatorcontrib><description>Non-verbal vocal interaction (NVVI) is an interaction method in which sounds other than speech produced by a human are used, such as humming. NVVI complements traditional speech recognition systems with continuous control. In order to combine the two approaches (e.g. "volume up, mmm") it is necessary to perform a speech/NVVI segmentation of the input sound signal. This paper presents two novel methods of speech and humming segmentation. The first method is based on classification of MFCC and RMS parameters using a neural network (MFCC method), while the other method computes volume changes in the signal (IAC method). The two methods are compared using a corpus collected from 13 speakers. The results indicate that the MFCC method outperforms IAC in terms of accuracy, precision, and recall.</description><identifier>ISSN: 1210-2512</identifier><language>eng</language><publisher>Spolecnost pro radioelektronicke inzenyrstvi</publisher><subject>MFCC ; Multi-layer perceptron ; Neural network ; Non-verbal vocal interaction ; Segmentation ; Speech</subject><ispartof>Radioengineering, 2012-09, Vol.21 (3), p.923-929</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>A. J. Sporka</creatorcontrib><creatorcontrib>O. Polacek</creatorcontrib><creatorcontrib>J. Havlik</creatorcontrib><title>Segmentation of Speech and Humming in Vocal Input</title><title>Radioengineering</title><description>Non-verbal vocal interaction (NVVI) is an interaction method in which sounds other than speech produced by a human are used, such as humming. NVVI complements traditional speech recognition systems with continuous control. In order to combine the two approaches (e.g. "volume up, mmm") it is necessary to perform a speech/NVVI segmentation of the input sound signal. This paper presents two novel methods of speech and humming segmentation. The first method is based on classification of MFCC and RMS parameters using a neural network (MFCC method), while the other method computes volume changes in the signal (IAC method). The two methods are compared using a corpus collected from 13 speakers. The results indicate that the MFCC method outperforms IAC in terms of accuracy, precision, and recall.</description><subject>MFCC</subject><subject>Multi-layer perceptron</subject><subject>Neural network</subject><subject>Non-verbal vocal interaction</subject><subject>Segmentation</subject><subject>Speech</subject><issn>1210-2512</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNotjc1Kw0AURmeh0Fr7DvMCgdx7J5nMUoraQMFF1W2Y3zglmQlJuvDtLerqg3PgfHdsCwhlgRXghj0sy6Usa8Cq3DI4-370adVrzInnwM-T9_aL6-T48TqOMfU8Jv6ZrR54m6br-sjugx4Wv__fHft4eX4_HIvT22t7eDoVDhHWAoyhRhK5SiBo51VdKRcaMjfdlBLBGeMgWNGEIIV3lhRYd_uW6ENQSDvW_nVd1pdumuOo5-8u69j9gjz3nZ7XaAffSQKywetaKSGC8AYJgiaoRSOMlIp-ANI0S7I</recordid><startdate>20120901</startdate><enddate>20120901</enddate><creator>A. J. Sporka</creator><creator>O. Polacek</creator><creator>J. Havlik</creator><general>Spolecnost pro radioelektronicke inzenyrstvi</general><scope>DOA</scope></search><sort><creationdate>20120901</creationdate><title>Segmentation of Speech and Humming in Vocal Input</title><author>A. J. Sporka ; O. Polacek ; J. Havlik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d221t-1bb38733d5421ade9659df83bd2280721dbbd1fc48ff74edc391cdeec72eff923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>MFCC</topic><topic>Multi-layer perceptron</topic><topic>Neural network</topic><topic>Non-verbal vocal interaction</topic><topic>Segmentation</topic><topic>Speech</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>A. J. Sporka</creatorcontrib><creatorcontrib>O. Polacek</creatorcontrib><creatorcontrib>J. Havlik</creatorcontrib><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Radioengineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>A. J. Sporka</au><au>O. Polacek</au><au>J. Havlik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Segmentation of Speech and Humming in Vocal Input</atitle><jtitle>Radioengineering</jtitle><date>2012-09-01</date><risdate>2012</risdate><volume>21</volume><issue>3</issue><spage>923</spage><epage>929</epage><pages>923-929</pages><issn>1210-2512</issn><abstract>Non-verbal vocal interaction (NVVI) is an interaction method in which sounds other than speech produced by a human are used, such as humming. NVVI complements traditional speech recognition systems with continuous control. In order to combine the two approaches (e.g. "volume up, mmm") it is necessary to perform a speech/NVVI segmentation of the input sound signal. This paper presents two novel methods of speech and humming segmentation. The first method is based on classification of MFCC and RMS parameters using a neural network (MFCC method), while the other method computes volume changes in the signal (IAC method). The two methods are compared using a corpus collected from 13 speakers. The results indicate that the MFCC method outperforms IAC in terms of accuracy, precision, and recall.</abstract><pub>Spolecnost pro radioelektronicke inzenyrstvi</pub><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1210-2512 |
ispartof | Radioengineering, 2012-09, Vol.21 (3), p.923-929 |
issn | 1210-2512 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_7313cfea69944f4eb231fa316484b779 |
source | IngentaConnect Journals |
subjects | MFCC Multi-layer perceptron Neural network Non-verbal vocal interaction Segmentation Speech |
title | Segmentation of Speech and Humming in Vocal Input |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T23%3A18%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-doaj&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Segmentation%20of%20Speech%20and%20Humming%20in%20Vocal%20Input&rft.jtitle=Radioengineering&rft.au=A.%20J.%20Sporka&rft.date=2012-09-01&rft.volume=21&rft.issue=3&rft.spage=923&rft.epage=929&rft.pages=923-929&rft.issn=1210-2512&rft_id=info:doi/&rft_dat=%3Cdoaj%3Eoai_doaj_org_article_7313cfea69944f4eb231fa316484b779%3C/doaj%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-d221t-1bb38733d5421ade9659df83bd2280721dbbd1fc48ff74edc391cdeec72eff923%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |