Proceedings Fonetik 2009 - Institutionen för lingvistik

More documents

Recommendations

Info

Proceedings, FONETIK 2009, Dept. of Linguistics, Stockholm Universitycordings were carried out in 44.1 KHz with 24-bit resolution. Totally, 15,604 utterances wererecorded.Table 1. A comparison of the length of the recordedspeech databases for different categories, Norwegianand SwedishNorwegian SwedishTotal time 26:03:15 28:27:24Total time (speech) 18:24:39 16:15:09Segments 568 606 781 769Phones 519 065 660 349Words 118 104 132 806Sentences 15 604 14 788A comparison of the figures above shows thatthe Swedish speaker is about 45% faster thanthe Norwegian speaker (11.37 vs. 7.83 phonesper second). This will result in very large filesetsfor the Norwegian textbooks, which oftenconsists of more than 400 pages, and a veryslow speech rate of the synthetic speech. However,the speech rate can be adjusted in the student'sDAISY-player or by the TTS-system itself.On the other hand, a slow speech ratecomes with the benefit that it well articulatedand clear speech can be attained in a more naturalway compared to slowing down a voice withan inherently fast speech rate.SegmentationUnlike the Swedish voice, for which all recordingswere automatically and manually segmented(Ericsson et al., 2007), all the Norwegianutterances were control listened, and thephonetic transcriptions were corrected beforethe automatic segmentation was done. In thatway, only the pronunciation variants that actuallyoccurred in the audio had to be taken intoaccount by the speech recognition tool(Sjölander, 2003). Another difference from theSwedish voice is that plosives are treated as onecontinuous segment, instead of being split intoobstruction and release.Misplaced phone boundaries and incorrectphone assignments will possibly be corrected inthe quality assurance project.Unit selection and concatenationThe unit selection method used in the Filibustersystem is based mainly on phone decision trees,which find candidates with desired properties,and strives to find as long phone sequences aspossible to minimise the number of concatenationpoints. The optimal phone sequence is chosenusing an optimisation technique, whichlooks at the phone's joining capability, as well asits spectral distance from the mean of all candidates.The best concatenation point betweentwo sound clips is found by correlating theirwaveforms.Text processingThe Swedish text processing system was usedas a base for the Norwegian system. Althoughthe two languages are similar in many ways,many modifications were needed.The tokenisation (at sentence, multi-wordand word level) is largely the same for Swedishand Norwegian. One of the exceptions is thesentence division at ordinals, where the standardNorwegian annotation is to mark the digit witha period as in '17. mai', which is an annotationthat is not used in Swedish.The Swedish part-of-speech tagging is doneby a hybrid tagger, a statistical tagger that usesthe POS trigrams of the Swedish SUC2.0 corpus(Källgren et al., 2006), and a rule-basedcomplement which handles critical part-ofspeechdisambiguation. It should be mentionedthat the aim of the inclusion of a part-of-speechtagger is not to achieve perfectly tagged sentences;its main purpose is to disambiguatehomographs. Although the Swedish and Norwegianmorphology and syntax differ, theSwedish tagger and the SUC trigram statisticsshould be used also for the Norwegian system,even though it seems like homographs in Norwegianbokmål need more attention than inSwedish. As an example, the relatively frequentNorwegian homographs where one form is anoun and the other a verb or a past participle,for example 'laget', in which the supine verbform (or past participle) is pronounced with the't' and with accent II ["lɑ:.gət], while the nounform is pronounced without the 't' and with accentI ['lɑ:.gə]. As it stands, it seems as the systemcan handle these cases to satisfaction. OOVwords are assigned their part-of-speech accordingto language specific statistics of suffixes ofdifferent lengths, and from contextual rules. Nophrase parsing is done for Norwegian, but thereis a future option to predict phrase boundariesfrom the part-of-speech tagged sentence.Regarding text identification, that is classifyingtext chunks as numerals or ordinals, years,intervals, acronyms, abbreviations, email addressesor URLs, formulas, biographical, biblicalor law references, name initials and so on,the modifications mainly involved translation offor instance units and numeral lists, new lists of38
Proceedings, FONETIK 2009, Dept. of Linguistics, Stockholm Universityabbreviations and formats for ordinals, date expressionsand suchlike. Similar modificationswere carried out for text expansions of theabove-mentioned classifications.A language detector that distinguishes thetarget language from English was also includedin the Norwegian system. This module looks upthe words in all dictionaries and suggests languagetag (Norwegian or English) for eachword depending on unambiguous languagetypes of surrounding words.OOV words are automatically predicted tobe proper names, simplex or compound Norwegianwords or English words. Some of the pronunciationsof these words are generated byrules, but the main part of the pronunciations isgenerated with CART trees, one for each wordtype.The output from the text processor is sent tothe TTS engine in SSML format.Quality assuranceThe quality assurance phase consists of twoparts, the developers’ own testing to catch generalerrors, and a listening test period where nativespeakers report errors in segmentation,pronunciation and text analysis to the developingteam. They are also able to correct minorerrors by adding or changing transcriptions orediting simpler text processing rules. Some tentextbooks will be produced for this purpose, aswell as test documents with utterances of highcomplexity.Black A. and Taylor P. (1997). Automaticallyclustering similar units for unit selection inspeech synthesis. Proceedings of Eurospeech97, Rhodes, Greece.DAISY Pipeline (2009).http://www.daisy.org/projekcts/pipeline.Ericsson C., Klein J., Sjölander K. and SönneboL. (2007). Filibuster – a new Swedish textto-speechsystem. Proceedings of Fonetik,TMH-QPSR 50(1), 33-36, Stockholm.Kominek J. and Black A. (2003). CMUARCTIC database for speech synthesis.Language Technologies Institute. CarnegieMellon University, Pittsburgh PA . TechnicalReport CMU-LTI-03-177.http://festvox.org/cmu_arctic/cmu_arctic_report.pdfKällgren G., Gustafson-Capkova S. and HartmanB. (2006). Stockholm Umeå Corpus2.0 (SUC2.0). Department of Linguistics,Stockholm University, Stockholm.Sjölander, K. (2003). An HMM-based systemfor automatic segmentation and alignment ofspeech. Proceedings of Fonetik 2003, 93-96,Stockholm.Sjölander K., Sönnebo L. and Tånnander C.(2008). Recent advancements in the Filibustertext-to-speech system. SLTC 2008.Øverland H. (2000). Transcription Conventionsfor Norwegian. Technical Report. NordiskSpråkteknologi ASCurrent statusThe second phase of the quality assurance phasewith native speakers will start in May 2009. Thesystem is scheduled to be put in production ofNorwegian textbooks by the autumn term of2009. Currently, the results are promising. Thevoice appears clear and highly intelligible, alsoin the generation of more complex utterancessuch as code switching between Norwegian andEnglish.References39
Page 1 and 2: Department of LinguisticsProceeding
Page 3 and 4: Proceedings, FONETIK 2009, Dept. of
Page 37: Proceedings, FONETIK 2009, Dept. of
Page 41 and 42: Proceedings, FOETIK 2009, Dept. of
Page 89 and 90:
Proceedings, FONETIK 2009, Dept. of
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Proceedings, FOETIK 2009, Dept. of
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227:
Department of LinguisticsPhonetics
show all

Proceedings Fonetik 2009 - Institutionen för lingvistik

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?