Segmentation and Labelling of Slovenian Diphone Inventories

Segmentation and Labelling of Slovenian Diphone Inventories* Jerneja Gros, Ivo Ip~i~, Simon Dobri~ek, France Miheli~, Nikola Pavegi~ Faculty of Electrical Engineering University of Ljubljana Tr~agka cesta 25 SI-1000 Ljubljana Slovenia jerneja.gros @ fe.uni-lj.si Abstract Preparation, recording, segmentation and pitch labelling of Slovenian diphone inventories are described. A special user friendly intert'ace package was developed in order to facilitate these operations. As acquisition of a labelled diphone inventory or adaptation of a speech synthesis system to synthesise further voices is manually intensive, an automatic procedure is required. A speech recogniser, based on Hidden Markov Models in forced segmenta- tion mode is used to outline phone boundaries within spoken logatoms. A statistical evalua- tion of manual and automatic segmentation dis- crepancies is performed so as to estinmte the reliability of automatically derived labels. Fi- nally, diphone boundaries are determined and pitch markers are assigned to voiced sections of the speech signal. 1 Introduction For the Slovenian language, several attempts were made in the past, where different aspects of a Slove- nian text-to-speech synthesis (TI'S) system were co- vered (Dobnikar95). Nevertheless, none of them suc- ceeded in building a complete system, providing high quality synthetic speech. In the Laboratory of Artifi- cial Pemeption, we started on text-to-speech synthesis one year ago (Gros96). Here we describe the acquisi- tion of an appropriate diphone inventory in a first ver- sion of our Slovenian TTS system, which is supposed to serve as a reference system for future improvements. We start with a brief overview of the different mo- dules el' the Slovenian TTS system, then we go on to describe how the existing diphone inventory was ob- tained. The acquisition era labelled diphone inventory or adaptation of a speech synthesis system to synthe- sise new voices is manually intensive and prone to er- rors, therefore automatic procedures are required (Tay- lor91, Sehmidt93, Cosi91, Ottesen93). In section 4 we *This work was partly funded by the Commission of the European Community under COP-94 contract No 01634 (SQEL) explain how we intend to automatically derive addi- tional diphone inventories for building new synthetic voices. 2 Slovenian TTS system Tile different phases of the text-to-speech transforma- tion are performed by separate independent modules, operating sequentially, as shown in Figure 1. Thus in- put text is gradually transformed into its spoken equi- valent, Graphemc-to-phoneme transcription First, abbreviations are expanded to t'ornl equiva- lent full words using a special list of lexical en- tries. A text pre-processor converts further special for- mats, like numbers or dates, into standard graphemic strings. Next, word pronunciation is derived, based on a user extensible pronunciation dictionary and letter- to-sound rules. The dictionary is supposed to cover the most fl'equent words in a given hmguage and a second dictionary helps with pronouncing proper names. Prosody generation Prosody generation assigns the sequence of allo- phones with some of their prosodic parameters (pitch l'requency, duration). First, words are syllabitied by counting the nmnber of their vowel clusters and dura- tion of syllables is modelled according to the speaker's …

Content truncated to 500 words