Skip to main content

Dnn-Based Speech Recognition For Globalphone Languages

Martha Yifiru Tachbelie, Ayimunishagu Abulimiti, Solomon Teferra Abate, Tanja Schultz

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
    Length: 14:59
04 May 2020

This paper describes new reference benchmark results based on hybrid Hidden Markov Model and Deep Neural Networks (HMM-DNN) for the GlobalPhone (GP) multilingual text and speech database. GP is a multilingual database of high-quality read speech with corresponding transcriptions and pronunciation dictionaries in more than 20 languages. Moreover, we provide new results for five additional languages, namely, Amharic, Oromo, Tigrigna, Wolaytta, and Uyghur. Across the 22 languages considered, the hybrid HMM-DNN models outperform the HMM-GMM based models regardless of the size of the training speech used. Overall, we achieved relative improvements that range from 7.14 to 59.43.

Value-Added Bundle(s) Including this Product

More Like This

  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00
  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00
  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00