Improving a tone labeling algorithm for Sesotho.

We report on a study that aimed to improve an existing tone label prediction algorithm for Sesotho, an official language of South Africa. Tone is an important prosodic feature of Sesotho, since speakers use tone to distinguish meaning. In order to implement tone in a Text-to-Speech system for Sesoth...

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Publicado en:Language Resources & Evaluation Vol. 49; no. 1; pp. 19 - 51
Autores principales: Raborife, Mpho, Ewert, Sigrid, Zerbian, Sabine
Formato: Artículo
Publicado: Springer Nature Mar2015
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Acceso en línea:Ver este registro en EBSCOhost
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          Raborife, Mpho
          Ewert, Sigrid
          Zerbian, Sabine
        affil:
          School of Computer Science, University of the Witwatersrand, Johannesburg South Africa
          Institute of Linguistics: English, University of Stuttgart, Stuttgart Germany
      su:
        Sotho language
        Linguistics
        Speech processing systems
        Orthography & spelling
        Computer science
        South Africa
      sug:
        subj:
          South Africa
          Sotho language
          Linguistics
          Speech processing systems
          Orthography & spelling
          Computer science
      keyword:
        Algorithm
        Bantu languages
        Sesotho
        Text-to-Speech systems
        Tone
      ab: We report on a study that aimed to improve an existing tone label prediction algorithm for Sesotho, an official language of South Africa. Tone is an important prosodic feature of Sesotho, since speakers use tone to distinguish meaning. In order to implement tone in a Text-to-Speech system for Sesotho, a tone modeling algorithm must receive as input the tone labels of the syllables of each word. Then it can predict the appropriate intonation of the word. Since Sesotho does not mark tone labels in orthography, the labels have to be predicted according to the tonal rules of the language. The existing tone label prediction algorithm has two drawbacks, namely it implements three tonal rules only and is restricted to the clitic phrase domain. In our study, we developed an algorithm that implements four additional tonal rules and addresses all parts of speech. The results show that the latter algorithm significantly improves the existing one by increasing the number of matched tone labels.
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