In this paper, we design and develop an intelligible and natural sounding corpus-based concatenative speech synthesis system for Turkish. The implemented system contains a front-end comprised of text analysis, phonetic analysis, and optional use of transplanted prosody. The unit selection algorithm is based on commonly used Viterbi decoding algorithm. The back-end is the speech waveform generation based on the harmonic coding of speech and overlapand-add mechanism. In this study, a Turkish phoneme set has been designed and a pronunciation lexicon for root words has been constructed. For assessing the intelligibility of the synthesized speech, a DRT word list for Turkish has been compiled. The developed system obtained 4.2 Mean Opinion Score (MOS) in the listening tests. 1.

This paper presents the first stochastic finite-state morphological parser for Turkish. The non-probabilistic parser is a standard finite-state transducer implementation of two-level morphology formalism. A disambiguated text corpus of 200 million words is used to stochastize the morphotactics transducer, then it is composed with the morphophonemics transducer to get a stochastic morphological parser. We present two applications to evaluate the effectiveness of the stochastic parser; spelling correction and morphology-based language modeling for speech recognition. 1