Application of non-negative frequency-weighted energy operator for vowel region detection
In this paper, a novel technique has been proposed for the vowel region detection from the continuous speech using an envelope of the derivative of the speech signal, which is a non-negative, frequency-weighted energy operator. The proposed vowel region detection method is implemented using a two-st...
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Veröffentlicht in: | International journal of speech technology 2018-06, Vol.21 (2), p.279-291 |
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description | In this paper, a novel technique has been proposed for the vowel region detection from the continuous speech using an envelope of the derivative of the speech signal, which is a non-negative, frequency-weighted energy operator. The proposed vowel region detection method is implemented using a two-stage algorithm. The first stage of vowel region detection consists of speech signal analysis to detect vowel onset points (VOP) and vowel end-points (VEP) using an instantaneous energy contour obtained from the envelope of the derivative of a speech signal. The VOPs and VEPs are spotted using the peak-finding algorithm based upon the first order Gaussian differentiator. The next stage consists of removal of spurious vowel regions and the correction of hypothesized VOP and VEP locations using combined cues obtained from the uniformity of epoch intervals and strength of the excitation of the speech signal. Performance of the proposed method for detecting vowel regions from the speech signal is evaluated using TIMIT acoustic-phonetic speech corpus. The proposed approach resulted in significantly high detection rate and less false alarm rate compared to the state-of-the-art methods in both clean and noisy environments. |
doi_str_mv | 10.1007/s10772-018-9505-x |
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The proposed vowel region detection method is implemented using a two-stage algorithm. The first stage of vowel region detection consists of speech signal analysis to detect vowel onset points (VOP) and vowel end-points (VEP) using an instantaneous energy contour obtained from the envelope of the derivative of a speech signal. The VOPs and VEPs are spotted using the peak-finding algorithm based upon the first order Gaussian differentiator. The next stage consists of removal of spurious vowel regions and the correction of hypothesized VOP and VEP locations using combined cues obtained from the uniformity of epoch intervals and strength of the excitation of the speech signal. Performance of the proposed method for detecting vowel regions from the speech signal is evaluated using TIMIT acoustic-phonetic speech corpus. 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The proposed approach resulted in significantly high detection rate and less false alarm rate compared to the state-of-the-art methods in both clean and noisy environments.</description><subject>Acoustic noise</subject><subject>Acoustic phonetics</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Continuous speech</subject><subject>Cues</subject><subject>Energy consumption</subject><subject>Engineering</subject><subject>False alarms</subject><subject>Operators (mathematics)</subject><subject>Signal analysis</subject><subject>Signal,Image and Speech Processing</subject><subject>Social Sciences</subject><subject>Speech</subject><subject>Speech disorders</subject><subject>Voice recognition</subject><subject>Vowels</subject><issn>1381-2416</issn><issn>1572-8110</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1UMlqwzAQFaWFpssH9GboWe1ItrUcQ-gGgV5y6Uk49sh1SCVXcra_r4wLPfUwzMJ7b2YeIXcMHhiAfIwMpOQUmKK6hJIez8iMlWmiGIPzVOeKUV4wcUmuYtwAgJaaz8jHvO-3XV0NnXeZt5nzjjpsU7_HzAb83qGrT_SAXfs5YJOhw9CeMt9jqAYfMpti7w-4zQK2o0aDA9aj2g25sNU24u1vviar56fV4pUu31_eFvMlrXPFByp1rQqegxLrvGmsAKuhtlLmFRPQYCU1CKbANlzIMv2oi7VUWhScNyXX6_ya3E-yffDp2DiYjd8FlzYaDrmSogClEopNqDr4GANa04fuqwonw8CMBprJQJMMNKOB5pg4fOLEhHUthj_l_0k_8t50BA</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Thirumuru, Ramakrishna</creator><creator>Vuppala, Anil Kumar</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7T9</scope></search><sort><creationdate>20180601</creationdate><title>Application of non-negative frequency-weighted energy operator for vowel region detection</title><author>Thirumuru, Ramakrishna ; Vuppala, Anil Kumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c382t-79c8423086b3ddf60f90cf773a160dea7906180fd267507794b7896422d529b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Acoustic noise</topic><topic>Acoustic phonetics</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Continuous speech</topic><topic>Cues</topic><topic>Energy consumption</topic><topic>Engineering</topic><topic>False alarms</topic><topic>Operators (mathematics)</topic><topic>Signal analysis</topic><topic>Signal,Image and Speech Processing</topic><topic>Social Sciences</topic><topic>Speech</topic><topic>Speech disorders</topic><topic>Voice recognition</topic><topic>Vowels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Thirumuru, Ramakrishna</creatorcontrib><creatorcontrib>Vuppala, Anil Kumar</creatorcontrib><collection>CrossRef</collection><collection>Linguistics and Language Behavior Abstracts (LLBA)</collection><jtitle>International journal of speech technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Thirumuru, Ramakrishna</au><au>Vuppala, Anil Kumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of non-negative frequency-weighted energy operator for vowel region detection</atitle><jtitle>International journal of speech technology</jtitle><stitle>Int J Speech Technol</stitle><date>2018-06-01</date><risdate>2018</risdate><volume>21</volume><issue>2</issue><spage>279</spage><epage>291</epage><pages>279-291</pages><issn>1381-2416</issn><eissn>1572-8110</eissn><abstract>In this paper, a novel technique has been proposed for the vowel region detection from the continuous speech using an envelope of the derivative of the speech signal, which is a non-negative, frequency-weighted energy operator. The proposed vowel region detection method is implemented using a two-stage algorithm. The first stage of vowel region detection consists of speech signal analysis to detect vowel onset points (VOP) and vowel end-points (VEP) using an instantaneous energy contour obtained from the envelope of the derivative of a speech signal. The VOPs and VEPs are spotted using the peak-finding algorithm based upon the first order Gaussian differentiator. The next stage consists of removal of spurious vowel regions and the correction of hypothesized VOP and VEP locations using combined cues obtained from the uniformity of epoch intervals and strength of the excitation of the speech signal. Performance of the proposed method for detecting vowel regions from the speech signal is evaluated using TIMIT acoustic-phonetic speech corpus. 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subjects | Acoustic noise Acoustic phonetics Algorithms Artificial Intelligence Continuous speech Cues Energy consumption Engineering False alarms Operators (mathematics) Signal analysis Signal,Image and Speech Processing Social Sciences Speech Speech disorders Voice recognition Vowels |
title | Application of non-negative frequency-weighted energy operator for vowel region detection |
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