Audio-Based Emotion Recognition from Natural Conversations Based on Co-Occurrence Matrix and Frequency Domain Energy Distribution Features View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Aya Sayedelahl , Pouria Fewzee , Mohamed S. Kamel , Fakhri Karray

ABSTRACT

Emotion recognition from natural speech is a very challenging problem. The audio sub-challenge represents an initial step towards building an efficient audio-visual based emotion recognition system that can detect emotions for real life applications (i.e. human-machine interaction and/or communication). The SEMAINE database, which consists of emotionally colored conversations, is used as the benchmark database. This paper presents our emotion recognition system from speech information in terms of positive/negative valence, and high and low arousal, expectancy and power. We introduce a new set of features including Co-Occurrence matrix based features as well as frequency domain energy distribution based features. Comparisons between well-known prosodic and spectral features and the new features are presented. Classification using the proposed features has shown promising results compared to the classical features on both the development and test data sets. More... »

PAGES

407-414

References to SciGraph publications

  • 2011. AVEC 2011–The First International Audio/Visual Emotion Challenge in AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION
  • 1995. The Nature of Statistical Learning Theory in NONE
  • Book

    TITLE

    Affective Computing and Intelligent Interaction

    ISBN

    978-3-642-24570-1
    978-3-642-24571-8

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-24571-8_52

    DOI

    http://dx.doi.org/10.1007/978-3-642-24571-8_52

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1002865356


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Psychology", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Psychology and Cognitive Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sayedelahl", 
            "givenName": "Aya", 
            "id": "sg:person.010134233275.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010134233275.00"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fewzee", 
            "givenName": "Pouria", 
            "id": "sg:person.012052167163.22", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012052167163.22"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kamel", 
            "givenName": "Mohamed S.", 
            "id": "sg:person.01133760566.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133760566.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Karray", 
            "givenName": "Fakhri", 
            "id": "sg:person.010544641574.33", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010544641574.33"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-24571-8_53", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009564678", 
              "https://doi.org/10.1007/978-3-642-24571-8_53"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.specom.2006.04.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015388048"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4757-2440-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027312764", 
              "https://doi.org/10.1007/978-1-4757-2440-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4757-2440-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027312764", 
              "https://doi.org/10.1007/978-1-4757-2440-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1071-5819(02)00141-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045187484"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1071-5819(02)00141-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045187484"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/79.911197", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061232074"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tassp.1985.1164511", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061519563"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tsmc.1973.4309314", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061792707"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/fg.2011.5771359", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094246775"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iita.2009.427", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094435395"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iconip.1999.845644", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095015844"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icassp.2007.367230", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095235312"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2011", 
        "datePublishedReg": "2011-01-01", 
        "description": "Emotion recognition from natural speech is a very challenging problem. The audio sub-challenge represents an initial step towards building an efficient audio-visual based emotion recognition system that can detect emotions for real life applications (i.e. human-machine interaction and/or communication). The SEMAINE database, which consists of emotionally colored conversations, is used as the benchmark database. This paper presents our emotion recognition system from speech information in terms of positive/negative valence, and high and low arousal, expectancy and power. We introduce a new set of features including Co-Occurrence matrix based features as well as frequency domain energy distribution based features. Comparisons between well-known prosodic and spectral features and the new features are presented. Classification using the proposed features has shown promising results compared to the classical features on both the development and test data sets.", 
        "editor": [
          {
            "familyName": "D\u2019Mello", 
            "givenName": "Sidney", 
            "type": "Person"
          }, 
          {
            "familyName": "Graesser", 
            "givenName": "Arthur", 
            "type": "Person"
          }, 
          {
            "familyName": "Schuller", 
            "givenName": "Bj\u00f6rn", 
            "type": "Person"
          }, 
          {
            "familyName": "Martin", 
            "givenName": "Jean-Claude", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-642-24571-8_52", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-642-24570-1", 
            "978-3-642-24571-8"
          ], 
          "name": "Affective Computing and Intelligent Interaction", 
          "type": "Book"
        }, 
        "name": "Audio-Based Emotion Recognition from Natural Conversations Based on Co-Occurrence Matrix and Frequency Domain Energy Distribution Features", 
        "pagination": "407-414", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1002865356"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-642-24571-8_52"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "0da534551a503dd6a4509e69afcad4d1880abd1a95e693bcdeadf10b00467388"
            ]
          }
        ], 
        "publisher": {
          "location": "Berlin, Heidelberg", 
          "name": "Springer Berlin Heidelberg", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-642-24571-8_52", 
          "https://app.dimensions.ai/details/publication/pub.1002865356"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T10:07", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000377_0000000377/records_106813_00000000.jsonl", 
        "type": "Chapter", 
        "url": "https://link.springer.com/10.1007%2F978-3-642-24571-8_52"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-24571-8_52'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-24571-8_52'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-24571-8_52'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-24571-8_52'


     

    This table displays all metadata directly associated to this object as RDF triples.

    136 TRIPLES      23 PREDICATES      38 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-642-24571-8_52 schema:about anzsrc-for:17
    2 anzsrc-for:1701
    3 schema:author N44eb02eb38624df9a3e9a3a5fdec3960
    4 schema:citation sg:pub.10.1007/978-1-4757-2440-0
    5 sg:pub.10.1007/978-3-642-24571-8_53
    6 https://doi.org/10.1016/j.specom.2006.04.003
    7 https://doi.org/10.1016/s1071-5819(02)00141-6
    8 https://doi.org/10.1109/79.911197
    9 https://doi.org/10.1109/fg.2011.5771359
    10 https://doi.org/10.1109/icassp.2007.367230
    11 https://doi.org/10.1109/iconip.1999.845644
    12 https://doi.org/10.1109/iita.2009.427
    13 https://doi.org/10.1109/tassp.1985.1164511
    14 https://doi.org/10.1109/tsmc.1973.4309314
    15 schema:datePublished 2011
    16 schema:datePublishedReg 2011-01-01
    17 schema:description Emotion recognition from natural speech is a very challenging problem. The audio sub-challenge represents an initial step towards building an efficient audio-visual based emotion recognition system that can detect emotions for real life applications (i.e. human-machine interaction and/or communication). The SEMAINE database, which consists of emotionally colored conversations, is used as the benchmark database. This paper presents our emotion recognition system from speech information in terms of positive/negative valence, and high and low arousal, expectancy and power. We introduce a new set of features including Co-Occurrence matrix based features as well as frequency domain energy distribution based features. Comparisons between well-known prosodic and spectral features and the new features are presented. Classification using the proposed features has shown promising results compared to the classical features on both the development and test data sets.
    18 schema:editor N9e83b790e47d4e00b49a48aa44f0b4f2
    19 schema:genre chapter
    20 schema:inLanguage en
    21 schema:isAccessibleForFree false
    22 schema:isPartOf N3bade4d7639e4fea8b72265e7b08bdec
    23 schema:name Audio-Based Emotion Recognition from Natural Conversations Based on Co-Occurrence Matrix and Frequency Domain Energy Distribution Features
    24 schema:pagination 407-414
    25 schema:productId N443181dd39874656b3c10a2c21fa7f35
    26 N78b246681e9445b38d45f23a5c53a187
    27 N997f58e6a398474796b737e8fa05b2e4
    28 schema:publisher Nd4d4cadfb76443c0afdc1aa4fc08429a
    29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002865356
    30 https://doi.org/10.1007/978-3-642-24571-8_52
    31 schema:sdDatePublished 2019-04-16T10:07
    32 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    33 schema:sdPublisher N92e2576adf50433eb8e62c200d507c4c
    34 schema:url https://link.springer.com/10.1007%2F978-3-642-24571-8_52
    35 sgo:license sg:explorer/license/
    36 sgo:sdDataset chapters
    37 rdf:type schema:Chapter
    38 N3bade4d7639e4fea8b72265e7b08bdec schema:isbn 978-3-642-24570-1
    39 978-3-642-24571-8
    40 schema:name Affective Computing and Intelligent Interaction
    41 rdf:type schema:Book
    42 N3d78957aa7c44cffa32ab23ccd4043a1 rdf:first sg:person.01133760566.26
    43 rdf:rest N4c5cce2dd46248d795d14aa150435779
    44 N443181dd39874656b3c10a2c21fa7f35 schema:name dimensions_id
    45 schema:value pub.1002865356
    46 rdf:type schema:PropertyValue
    47 N44eb02eb38624df9a3e9a3a5fdec3960 rdf:first sg:person.010134233275.00
    48 rdf:rest N84af9c2d1558401393c3d1df09171c6e
    49 N496b0afc82754105b05ab17fc07f096a schema:familyName Graesser
    50 schema:givenName Arthur
    51 rdf:type schema:Person
    52 N4c5cce2dd46248d795d14aa150435779 rdf:first sg:person.010544641574.33
    53 rdf:rest rdf:nil
    54 N57ef3a20253a4342aab25c3a7329cf01 schema:familyName D’Mello
    55 schema:givenName Sidney
    56 rdf:type schema:Person
    57 N726018780c9945299e848a3357116aca rdf:first Ne785275236364fe2ad8059a054e8453e
    58 rdf:rest rdf:nil
    59 N78b246681e9445b38d45f23a5c53a187 schema:name doi
    60 schema:value 10.1007/978-3-642-24571-8_52
    61 rdf:type schema:PropertyValue
    62 N7df31a61a7f1430da1c976cd3e5153ce rdf:first N496b0afc82754105b05ab17fc07f096a
    63 rdf:rest Ne3795d581c9e4b55b8385072fff045d5
    64 N7f538c4278b2421fb4c214a07cc16bbd schema:familyName Schuller
    65 schema:givenName Björn
    66 rdf:type schema:Person
    67 N84af9c2d1558401393c3d1df09171c6e rdf:first sg:person.012052167163.22
    68 rdf:rest N3d78957aa7c44cffa32ab23ccd4043a1
    69 N92e2576adf50433eb8e62c200d507c4c schema:name Springer Nature - SN SciGraph project
    70 rdf:type schema:Organization
    71 N997f58e6a398474796b737e8fa05b2e4 schema:name readcube_id
    72 schema:value 0da534551a503dd6a4509e69afcad4d1880abd1a95e693bcdeadf10b00467388
    73 rdf:type schema:PropertyValue
    74 N9e83b790e47d4e00b49a48aa44f0b4f2 rdf:first N57ef3a20253a4342aab25c3a7329cf01
    75 rdf:rest N7df31a61a7f1430da1c976cd3e5153ce
    76 Nd4d4cadfb76443c0afdc1aa4fc08429a schema:location Berlin, Heidelberg
    77 schema:name Springer Berlin Heidelberg
    78 rdf:type schema:Organisation
    79 Ne3795d581c9e4b55b8385072fff045d5 rdf:first N7f538c4278b2421fb4c214a07cc16bbd
    80 rdf:rest N726018780c9945299e848a3357116aca
    81 Ne785275236364fe2ad8059a054e8453e schema:familyName Martin
    82 schema:givenName Jean-Claude
    83 rdf:type schema:Person
    84 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
    85 schema:name Psychology and Cognitive Sciences
    86 rdf:type schema:DefinedTerm
    87 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
    88 schema:name Psychology
    89 rdf:type schema:DefinedTerm
    90 sg:person.010134233275.00 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    91 schema:familyName Sayedelahl
    92 schema:givenName Aya
    93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010134233275.00
    94 rdf:type schema:Person
    95 sg:person.010544641574.33 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    96 schema:familyName Karray
    97 schema:givenName Fakhri
    98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010544641574.33
    99 rdf:type schema:Person
    100 sg:person.01133760566.26 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    101 schema:familyName Kamel
    102 schema:givenName Mohamed S.
    103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133760566.26
    104 rdf:type schema:Person
    105 sg:person.012052167163.22 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    106 schema:familyName Fewzee
    107 schema:givenName Pouria
    108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012052167163.22
    109 rdf:type schema:Person
    110 sg:pub.10.1007/978-1-4757-2440-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027312764
    111 https://doi.org/10.1007/978-1-4757-2440-0
    112 rdf:type schema:CreativeWork
    113 sg:pub.10.1007/978-3-642-24571-8_53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009564678
    114 https://doi.org/10.1007/978-3-642-24571-8_53
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1016/j.specom.2006.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015388048
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.1016/s1071-5819(02)00141-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045187484
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.1109/79.911197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061232074
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1109/fg.2011.5771359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094246775
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1109/icassp.2007.367230 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095235312
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1109/iconip.1999.845644 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095015844
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1109/iita.2009.427 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094435395
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1109/tassp.1985.1164511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061519563
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1109/tsmc.1973.4309314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061792707
    133 rdf:type schema:CreativeWork
    134 https://www.grid.ac/institutes/grid.46078.3d schema:alternateName University of Waterloo
    135 schema:name Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Canada
    136 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...