• DocumentCode
    717297
  • Title

    Intensity feature for speech stress detection

  • Author

    Czap, Laszlo ; Pinter, Judit Maria

  • Author_Institution
    Dept. of Autom. & Inf.-Commun., Univ. of Miskolc, Miskolc, Hungary
  • fYear
    2015
  • fDate
    27-30 May 2015
  • Firstpage
    91
  • Lastpage
    94
  • Abstract
    Suprasegmental features are fundamental properties of speech. They can improve not only the naturalness of synthesized speech, but the performance of machine speech recognition in voice controlled logistic systems. In linguistics, stress is the relative emphasis that may be given to certain syllables in a word, or to certain words in a phrase or sentence. The term is also used for similar patterns of phonetic prominence inside syllables. The stress placed on syllables within words is called word stress or lexical stress. The stress placed on words within sentences is called sentence stress or prosodic stress. Sentence and word stress are crucial prosodic features. One of the features usually used for stress detection is the energy of syllables, but the average energies of vowels are various. Energy of a stressed weak vowel can be lower than that of an unstressed strong vowel. We compare the amplitude of the actual vowel to that of its average to show the stressed or unstressed nature of the syllable. Average energies of vowels are obtained from a speech recognizer trained with voices of hundreds of speakers.
  • Keywords
    signal detection; speech recognition; speech synthesis; average vowel energy; intensity feature; lexical stress; linguistics; machine speech recognition; prosodic stress; sentence stress; speech stress detection; speech synthesis; suprasegmental features; voice controlled logistic systems; word stress; Hidden Markov models; Speech; acoustic feature extraction methods; speech recognition; stress detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Carpathian Control Conference (ICCC), 2015 16th International
  • Conference_Location
    Szilvasvarad
  • Print_ISBN
    978-1-4799-7369-9
  • Type

    conf

  • DOI
    10.1109/CarpathianCC.2015.7145052
  • Filename
    7145052