Matches in SemOpenAlex for { <https://semopenalex.org/work/W2023274067> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W2023274067 abstract "This paper presents a neural model of speaker identification using the vowel sound segmented out from words spoken by a speaker. Vowel sounds occur in a speech more frequently and with higher energy. Therefore, situations where acoustic information is noise corrupted vowel sounds can be used to extract different amounts of speaker discriminative information. The model explained here uses a neural framework formed with Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) where a novel Self Organizing Map (SOM) based vowel segmentation technique is used. The work extracts glottal source information of the speakers by Empirical-Mode Decomposition (EMD) of the speech signal and depending on which a LVQ based speaker code book is formed. The work shows the use of residual signal obtained from EMD of speech as a speaker discriminative feature. The neural approach of speaker identification gives superior performance in comparison to the conventional statistical approach like Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs) etc. found in literature. The work formulates a framework for the design of a ANN based speaker recognition model for Assamese language which is spoken by around three million people in the North East Indian state of Assam. Although the proposed model has been experimented in case of the speakers of Assamese language, it shall also be suitable for other Devanagari based languages for which the speaker database should contain samples of that specific language." @default.
- W2023274067 created "2016-06-24" @default.
- W2023274067 creator A5025157529 @default.
- W2023274067 creator A5074442858 @default.
- W2023274067 date "2013-08-01" @default.
- W2023274067 modified "2023-09-24" @default.
- W2023274067 title "Speaker identification model for Assamese language using a neural framework" @default.
- W2023274067 cites W1541754811 @default.
- W2023274067 cites W181306515 @default.
- W2023274067 cites W1964168965 @default.
- W2023274067 cites W1995978175 @default.
- W2023274067 cites W2007221293 @default.
- W2023274067 cites W2023840808 @default.
- W2023274067 cites W2057209169 @default.
- W2023274067 cites W2078277219 @default.
- W2023274067 cites W2096891735 @default.
- W2023274067 cites W2099096195 @default.
- W2023274067 cites W2117796963 @default.
- W2023274067 cites W2154087768 @default.
- W2023274067 cites W2159858406 @default.
- W2023274067 cites W2165880886 @default.
- W2023274067 cites W2296031562 @default.
- W2023274067 cites W3151121917 @default.
- W2023274067 doi "https://doi.org/10.1109/ijcnn.2013.6707000" @default.
- W2023274067 hasPublicationYear "2013" @default.
- W2023274067 type Work @default.
- W2023274067 sameAs 2023274067 @default.
- W2023274067 citedByCount "4" @default.
- W2023274067 countsByYear W20232740672014 @default.
- W2023274067 countsByYear W20232740672015 @default.
- W2023274067 countsByYear W20232740672016 @default.
- W2023274067 countsByYear W20232740672021 @default.
- W2023274067 crossrefType "proceedings-article" @default.
- W2023274067 hasAuthorship W2023274067A5025157529 @default.
- W2023274067 hasAuthorship W2023274067A5074442858 @default.
- W2023274067 hasConcept C133892786 @default.
- W2023274067 hasConcept C138885662 @default.
- W2023274067 hasConcept C151989614 @default.
- W2023274067 hasConcept C153180895 @default.
- W2023274067 hasConcept C154945302 @default.
- W2023274067 hasConcept C199833920 @default.
- W2023274067 hasConcept C23224414 @default.
- W2023274067 hasConcept C2776401178 @default.
- W2023274067 hasConcept C2777834912 @default.
- W2023274067 hasConcept C2779581591 @default.
- W2023274067 hasConcept C28490314 @default.
- W2023274067 hasConcept C40567965 @default.
- W2023274067 hasConcept C41008148 @default.
- W2023274067 hasConcept C41895202 @default.
- W2023274067 hasConcept C50644808 @default.
- W2023274067 hasConcept C52622490 @default.
- W2023274067 hasConcept C61224824 @default.
- W2023274067 hasConcept C97931131 @default.
- W2023274067 hasConceptScore W2023274067C133892786 @default.
- W2023274067 hasConceptScore W2023274067C138885662 @default.
- W2023274067 hasConceptScore W2023274067C151989614 @default.
- W2023274067 hasConceptScore W2023274067C153180895 @default.
- W2023274067 hasConceptScore W2023274067C154945302 @default.
- W2023274067 hasConceptScore W2023274067C199833920 @default.
- W2023274067 hasConceptScore W2023274067C23224414 @default.
- W2023274067 hasConceptScore W2023274067C2776401178 @default.
- W2023274067 hasConceptScore W2023274067C2777834912 @default.
- W2023274067 hasConceptScore W2023274067C2779581591 @default.
- W2023274067 hasConceptScore W2023274067C28490314 @default.
- W2023274067 hasConceptScore W2023274067C40567965 @default.
- W2023274067 hasConceptScore W2023274067C41008148 @default.
- W2023274067 hasConceptScore W2023274067C41895202 @default.
- W2023274067 hasConceptScore W2023274067C50644808 @default.
- W2023274067 hasConceptScore W2023274067C52622490 @default.
- W2023274067 hasConceptScore W2023274067C61224824 @default.
- W2023274067 hasConceptScore W2023274067C97931131 @default.
- W2023274067 hasLocation W20232740671 @default.
- W2023274067 hasOpenAccess W2023274067 @default.
- W2023274067 hasPrimaryLocation W20232740671 @default.
- W2023274067 hasRelatedWork W1646426535 @default.
- W2023274067 hasRelatedWork W2019724452 @default.
- W2023274067 hasRelatedWork W2023274067 @default.
- W2023274067 hasRelatedWork W203510659 @default.
- W2023274067 hasRelatedWork W2045795602 @default.
- W2023274067 hasRelatedWork W2167501319 @default.
- W2023274067 hasRelatedWork W2975896705 @default.
- W2023274067 hasRelatedWork W3005334943 @default.
- W2023274067 hasRelatedWork W4287871426 @default.
- W2023274067 hasRelatedWork W4288101870 @default.
- W2023274067 isParatext "false" @default.
- W2023274067 isRetracted "false" @default.
- W2023274067 magId "2023274067" @default.
- W2023274067 workType "article" @default.