• DocumentCode
    310659
  • Title

    An advanced system to generate pronunciations of proper nouns

  • Author

    Deshmukh, Neeraj ; Ngan, Julie ; Hamaker, Jonathan ; Picone, Joseph

  • Author_Institution
    Inst. for Signal & Inf. Process., Mississippi State Univ., MS, USA
  • Volume
    2
  • fYear
    1997
  • fDate
    21-24 Apr 1997
  • Firstpage
    1467
  • Abstract
    Accurate recognition of proper nouns is a critical component of automatic speech recognition (ASR). Since there are no obvious letter-to-sound conversion rules that govern the pronunciation of any large set of proper nouns, this is an open-ended problem that evolves constantly under various sociolinguistic influences. A Boltzmann machine neural network is well-suited for the task of generating the most likely pronunciations of a proper noun. This pronunciation output can be used to build better acoustic models for the noun that result in improved recognition performance. We present an advanced version of this N-best pronunciations system; and a multiple pronunciations dictionary of 18000 surnames and 25000 pronunciations used as a training database. The database and software are available in the public domain
  • Keywords
    Boltzmann machines; acoustic signal processing; learning (artificial intelligence); multilayer perceptrons; speech recognition; Boltzmann machine neural network; N-best pronunciations system; acoustic models; automatic speech recognition; multilayered perceptron; multiple pronunciations dictionary; open-ended problem; pronunciation generation; pronunciation output; proper nouns recognition; public domain software; recognition performance; surnames; training database; Automatic speech recognition; Backpropagation; Computational modeling; Databases; Dictionaries; Information processing; Neural networks; Neurons; Signal processing; Simulated annealing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
  • Conference_Location
    Munich
  • ISSN
    1520-6149
  • Print_ISBN
    0-8186-7919-0
  • Type

    conf

  • DOI
    10.1109/ICASSP.1997.596226
  • Filename
    596226