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\title{Rules for automatic grapheme-to-allophone transcription in Slovene}
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\author{Jerneja Gros \and France Miheli\v c \and Simon Dobri\v sek \and Toma\v z Erjavec \and Mario \v Zganec}
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%\authorrunning{Jerneja Gros et al.}   % abbreviated author list (for running head)
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\tocauthor {Jerneja Gros, France Miheli\v c, Nikola Pave\v si\'c (University of Ljubljana)}
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\institute{University of Ljubljana, Faculty od Electrical Engineering,\\ Tr\v za\v ska 25, SI-1000
Ljubljana, Slovenia\\
\email{nejka@fe.uni-lj.si},\\ WWW home page:
\texttt{http://luz.fe.uni-lj.si} }

\maketitle              % typeset the title of the contribution

\begin{abstract}
The domain of spoken language technologies ranges from speech 
input and output systems to complex understanding and generation 
systems, including multi- modal systems of widely differing 
complexity (such as automatic dictation machines) and multilingual 
systems (for example automatic dialogue and translation systems). 
The definition of standards and evaluation methodologies for such 
systems involves the specification and development of highly specific 
spoken language corpus and lexicon resources, and measurement and 
evaluation tools \cite{EAGLES Handbook}. This paper presents the 
MobiLuz spoken resources of the Slovene language, which will be 
made freely available for research purposes in speech technology 
and linguistics. 
\end{abstract}
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\section{Introduction}
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The domain of spoken language technologies ranges 
from speech input and output systems to complex 
understanding and generation systems, including multi- 
modal systems of widely differing complexity (such as 
automatic dictation machines) and multilingual systems 
(for example automatic dialogue and translation systems). 
The definition of standards and evaluation 
methodologies for such systems involves the specification 
and development of highly specific spoken language 
corpus and lexicon resources, and measurement and 
evaluation tools. 

In the beginning, standards for these areas have been 
derived from the consensus within the spoken language 
community previously established in a number of 
European and national projects, with reference to 
important initiatives in the US and Japan. 
Primary among these have been the SAM projects 
(centered on component technology assessment and 
corpus creation), SQALE (for large vocabulary systems 
assessment) and both SUNDIAL and SUNSTAR (for 
multi-modal systems.) 

Past and present projects with significant outputs in the 
domain of assessment and resources include ARS, 
RELATOR, ONOMASTICA and SPEECHDAT, as well 
as major national projects and programs of research such 
as VERBMOBIL in Germany. 
This has led to an initial documentation of existing 
practice which was relatively comprehensive but in many 
respects heterogeneous and widely dispersed. 
The lack of generic technologies and resources and the 
wide diversity of formats and specifications has hindered 
the effective reutilisation of existing resources. 
In 1993, the EAGLES (Expert Advisory Group on 
Language Engineering Standards) initiative was launched 
within the framework of the CEU's DGXIII Linguistic 
Research and Engineering (LRE) Programme, to 
accelerate the provision of standards for developing, 
exploiting and evaluating large-scale language resources. 
A special working group has been set up for this 
purpose, named the Spoken Language Working Group 
(SLWG). The project resulted in the publication of  
comprehensive guidelines documenting existing working 
practices in Europe and guidelines for spoken language 
resource creation and description \cite{EAGLES Handbook}.
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\section{Slovene Speech Corpora}
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For the Slovene language, several attempts in speech 
data collection were made in the past, resulting in various 
speech corpora: SNABI \cite{Kacic94,Kacic98}, 
LUZ diphones \cite{Gros96}, 
GOPOLIS \cite{Dobrisek98} and 
SPEECHDAT-Slovene \cite{Kaiser98}, distributed by ELRA.

The collected speech data mainly represented the 
domain of intended applications and are not available for 
distribution, except for the Slovenian SpeechDat(II) FDB-
1000 corpus containing phonetically rich sentences. The 
corpus consists of read and spontaneous speech and was 
recorded through an ISDN card (1.000 speakers). A 
phonetic lexicon with canonical transcriptions in SAMPA 
is also provided.
However, due to its high cost (20.000 EUR) the 
Slovene SPEECHDAT corpus can hardly be used for 
research or even development purposes. We therefore 
decided to create a collection of various Slovene speech 
data, freely available for research purposes in speech 
technology and linguistics. Such speech resources are 
essential for building multi-lingual speech recognition and 
text-to-speech synthesis applications. 

The idea is in the process of being finalized within the 
MobiLuz project funded by the Slovene Ministry of 
Science and Technology and the Slovene mobile 
telephony operator Mobitel d.d. 

The MobiLuz project collects and integrates resources 
of several previous project, e.g. the EU Copernicus SQEL 
and MULTEXT-East and will provide three main 
deliverables:\\
1.  MobiLuz Slovene speech corpus,\\
2. MobiLuz Slovene speech corpus annotations and 
lexicon and\\
3. MobiLuz speech tools.\\
The next three sections detail our work on these 
deliverables.
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\section{MobiLuz Slovene speech corpus}
The MobiLuz Slovene speech corpus includes various 
collections of speech data pronounced by multiple 
speakers, either as read speech or spontaneous speech. In 
particular, the corpus consists of the following 
components:\\
- a fully updated and revised version of the 50 speaker 
GOPOLIS corpus of air travel inquiries,\\
- isolated spoken commands (digits, common control 
commands etc.) and\\
- recordings of live dialogs.
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\subsection{Gopolis}
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The GOPOLIS corpus is a large multi-speaker speech 
database, derived from real situation dialogs concerning 
airline timetable information services. It was used as the 
Slovenian speech database within the SQEL project 
(Copernicus COP-94 contract No. 01634) for building a 
multi-lingual speech recognition and understanding dialog 
system \cite{Ipsic98}, capable of passing information 
over the telephone line to a client in one of four European 
languages - German, Czech, Slovak and Slovenian. 
The name of the database has been derived from 
"GOvorjena POizvedovanja o Letalskih Informacijah v 
Slovenskem jeziku", meaning "Spoken Flight Information 
Queries in the Slovene Language".

The sentence corpus was drawn from listening to 
recordings of real situation dialogs between anonymous 
clients' inquiries and telephone operators at the Adria 
Airways information center (15 hrs of speech stored on 
audiotapes).

The selected 300 typical sentences were compiled into 
the form of rewrite rules to obtain a generative sentence 
pattern grammar \cite{Gros95}. Using this grammar, 
we produced 22,500 different sentences for short 
introductory inquiries, long inquiries and short 
confirmations. 5,077 of them were selected to form the 
final sentence corpus.

Each of the total of 50 speakers (25 female and 25 
male) read about 100 randomly selected unique corpus 
sentences and 71 sentences of welcome greetings, 
introductory phrases, short affirmations and farewell 
greetings, common to all of the speakers. Each session has 
a list of attributes with speaker and recording session 
descriptors.
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\subsection{Isolated Commands}
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A set of isolated commands spoken by various 
speakers has also been recorded. Included are the most 
common commands used in telecommunication 
applications and commands for navigating windows 
applications.
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\subsection{Recording conditions}
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The recording sessions were performed in a normal 
laboratory acoustic environment. Additional noise, such as 
background speech or slamming doors was avoided.
The utterances were acquired by a close talking 
microphone and simultaneously by telephone. Thus an 
additional analysis of both audio devices was enabled. 

A set of recording environment programs was 
developed for the HP 9000 workstation platform. The user 
interface program built for recording communicates with 
two audio servers over the network, displays and saves the 
acquired signals and displays the sentences that a speaker 
should utter. So the acoustic realizations are in 
form of read continuous speech.
The program is also equipped with loudness detection 
and an acoustic messaging system. It takes care of the 
correct maximum loudness level, begin and end pauses 
and synchronizes the acquired telephone and microphone 
speech signals. 

The audio servers use the HP 9000/735 common audio 
hardware components and the additional Gradient 
Technology DeskLab hardware with a full telephone 
interface (DeskLab is a data acquisition and play device, 
which communicates via SCSI with a workstation). 
The speaker has to press a space bar key to signal the 
start of the recording session and then again to finish it. 
The program requires at least half a second of silence at 
the beginning and at the end of the utterance. 
A sampling rate of 16 kHz for both microphone and 
telephone signals and a 16-bit data format with MSB-LSB 
byte order was chosen.
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\section {Corpus annotations and lexica}
The GOPOLIS corpus is encoded in accordance with 
TEI recommendations \cite{Sperberg94}, in particular, 
the base tagset for Transcriptions of 
Speech, the additional tagsets for Simple Analytic 
Mechanisms and Language Corpora, and some local 
modifications.  The corpus contains the TEI header, 
giving the File, Encoding and Profile descriptions. Here 
general information about the corpus, including speaker 
descriptions is given. The body of the corpus consists of 
the 5,077 sentences (utterances), each marked with an ID 
and references to its speakers.

The utterances are segmented into words and 
punctuation marks, and each word is given in its 
orthographic form, as well as in the automatically derived  
phonetic transcription. Furthermore, the words were 
automatically tagged for their lemma and morphosyntactic 
description. Some sample data will contain also prosodic 
annotations. 

Two lexica accompany the corpus: a pronunciation 
dictionary and a word-form lexicon with the 
morphosyntactic descriptions.
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\subsection{Transcription and tagging}
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The phonetic transcriptions in the corpus are based on 
the Slovene MRPA set \cite{Dobrisek96,Zemljak00} 
containing machine-readable phonetic symbols equivalent 
to the Slovene IPA symbols \cite{Sustarsic98}.

The morphosyntactic descriptions and lexicon are 
based on the MULTEXT-East (Slovene) tagset and 
lexicon  \cite{Dmitrova98,Ide98}.  The 
lexicon contains lemmas, their full inflectional paradigms 
and the morhosyntactic descriptions of the wordforms 
\cite{Erjavec98}. The descriptions have a feature-structure 
like format and encode informations such as part-of-
speech, number, case, etc.  These descriptions and lexicon 
are then used to automatically tag and lemmatise the 
corpus. The tagger used is TnT \cite{Brants00}, which had 
been trained on the Slovene MULTEXT-East corpus. 
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\subsection{Speech segmentation and alignment}
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The automatic segmentation is performed using the 
DTW based approach described in \cite{Dobrisek98} 
where the speech material is automatically segmented and 
labelled using dynamic time warping alignment of a 
natural utterance with a synthesised speech signal. 
The synthesis of speech signals was achieved by 
simply concatenating labelled diphone speech signals 
using a simplified TD-PSOLA technique. The diphone 
inventory used was borrowed from the Slovenian text-to-
speech system S5 \cite{Gros97}. The diphone inventory 
consists of a set of segmented and labelled diphone speech 
signals. Every diphone in the inventory is segmented into 
two separated phones and voiced phones were additionally 
marked with pitch period markers.
 
According to the phonetic transcription of the natural 
speech signal, a sequence of concatenated diphone speech 
signals was generated. A conventional DTW alignment of 
the utterance with the synthesised speech signal was 
performed with two sequences of feature vectors derived 
from both speech signals. This method has the advantage 
that we do not need labelled reference speech signals since 
we know the phone boundaries of the synthetic speech 
signal. 
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\section{Speech Tools}
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The MobiLuz speech corpus will be accompanied by 
the Sigmark software developed in cooperation with 
Masterpoint, a user-friendly program interface which 
allows manual editing, viewing and marker corrections of 
speech signals on various levels (phone, word and phrase 
levels). 

Markers can also be copied from one level to the other 
which proves to be very useful for synchronising marker 
sets on different levels.
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\section{Conclusion}
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The MobiLuz project is aiming to set up an 
infrastructure of Slovene spoken resources necessary for 
building various multi-lingual speech recognition and 
synthesis applications. For example, the GOPOLIS speech 
corpus, which will become available in the scope of  
MobiLuz, has already been successfully used for training 
of the HMM-based speech recognition engine in the 
multilingual automatic dialog system SQEL \cite{Ipsic98}.
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\section{Acknowledgements}
The development of MobiLuz speech corpus was 
supported by the Slovene mobile telephony operator 
Mobitel d.d. and grant of the Slovene Ministry of Science 
and Technology.  The development of the resources 
contained in the corpus have also been supported by the 
EU Copernicus projects SQEL and MULTEXT-East.
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% ---- Bibliography ----
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\begin{thebibliography}{11}
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\bibitem {Brants00}
Brants, T. (2000): TnT-A Statistical Part-of-Speech                                          
Tagger. Proceedings of the ANLP-NAACL, in print, 
Seattle.

\bibitem{Dobrisek96}
Dobri\v sek S., Ka\v ci\v c Z., Gros J., Horvat B. and Miheli\v c F., (1996):
An Initiative for Standardisation of Phonetic Transcription of Slovenian 
Speech, Proceedings of the Fifth Electrotechnical and Computer Science Conference ER
K'96, pp. 247--250,
Portoro\v z, Slovenia, 1996.

\bibitem {Dmitrova98}
Dimitrova, L., Erjavec, T. Ide, N. Kaalep, H.J., Petkevi\v c, 
V. and Tufis, D. (1998): Multext-East: Parallel and 
Comparable Corpora and Lexicons for Six Central and 
Eastern European Languages. COLING-ACL '98 
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\bibitem {Dobrisek98}
Dobri\v sek S., Gros J., Miheli\v c F. and Pave\v si\'c N. (1998): 
Recording  and Labelling of the GOPOLIS Slovenian 
Speech Database, Proceedings of the First International 
Conference on Language Resources and Evaluation, pp. 
1089-1096. Granada, Spain.   

\bibitem {EAGLES Handbook}
EAGLES Handbook (1997): Handbook of Standards and 
Resources for Spoken Language Systems. Editors D. 
Gibbon, Roger Moore and Richard Winski. Berlin: 
Mouton de Gruyter.

\bibitem {Erjavec98}
Erjavec T. (1998): The MULTEXT-East Slovene Lexicon. 
Proceedings of the ERK'98 Conference, Portoro\v z, 
Slovenia, pp. 189-192.

\bibitem{Gros95}
Gros J., Miheli\v c F. and Pave\v si\' c N., (1995):
Sentence Hypothesisation Using Ng-Gram Models,
In Proceedings of the the Fourth European Conference On Speech 
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Madrid, Spain.

\bibitem {Gros96}
Gros J., Ip\v si\'c I., Miheli\v c F. and Pave\v si\'c N. (1996): 
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\bibitem {Gros97}
Gros, J., Pave\v si\'c, N. and Miheli\v c, F. (1997): Text-to-speech 
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language. Journal of Computing and Information 
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\bibitem {Ide98}
Ide, N., Tufis, D. and Erjavec, T. (1998): Development and 
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\bibitem {Ipsic98}
Ip\v s\'c I., Miheli\v c F., Dobri\v sek S., Gros J. and Pave\v si\'c N. 
(1998): An overview of the spoken queries in European 
languages : the Slovenian spoken dialog system. 
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SQEL Workshop on Multi-Lingual Information 
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\bibitem {Kacic94}
Ka\v ci\v c Z. and Horvat B. and Derli\v c R. (1994): Zasnova 
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\bibitem {Kacic98}
Ka\v ci\v c Z. and Horvat B. (1998): Izgradnja infrastrukture, 
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jezik.  Proceedings of the Conference on Language 
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100-104.

\bibitem {Kaiser98}
Kaiser J. and Ka\v ci\v c Z. (1998): Development of Slovenian 
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\bibitem {Sperberg94}
Sperberg-McQueen, C.M., and Burnard, L., eds. (1994): 
Guidelines for Electronic Text Encoding and 
Interchange. Chicago and Oxford. 

\bibitem {Sustarsic98}
\v Su\v star\v si\v c R., Komar S. and Petek B. (1998): Slovene IPA 
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\bibitem {Zemljak00}
Zemljak M., Ka\v ci\v c Z., Dobri\v sek S. and Gros J. (2000): A 
Machine-readable Phonetic Transcription of the 
Slovene Speech, in preparation.


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