Name pronunciation in German text-to-speech synthesis
Stefanie Jannedy Bernd MSbius Linguistics Dept. Language Modeling Research Ohio State University Bell Laboratories Columbus, OH 43210, USA Murray Hill, NJ 07974, USA j annedy©ling, ohio-state, edu bmo©research, bell-labs, com
Abstract In this paper, we concentrate on street names be- cause they encompass interesting aspects of geo- We describe the name analysis and pro- graphical as well as of personal names. Linguistic de- nunciation component in the German ver- scriptions and criteria as well as statistical considera- sion of the Bell Labs multilingual text-to- tions, in the sense of frequency distributions derived speech system. We concentrate on street from a large database, were used in the construction names because they encompass interest- of the name analysis component. The system was ing aspects of geographical and personal implemented in the framework of finite-state trans- names. The system was implemented in ducer (FST) technology (see (Sproat, 1992) for a the framework of finite-state transducer discussion focussing on morphology). For evalua- technology, using linguistic criteria as well tion purposes, we compared the performances of the as frequency distributions derived from a generM-purpose text analysis and the name-specific database. In evaluation experiments, we systems on training and test materials. compared the performances of the general- As of now, we have neither attempted to deter- purpose text analysis and the name-specific mine the etymological or ethnic origin of names, nor system on training and test materials. The have we addressed the problem of detecting names name-specific system significantly outper- in arbitrary text. However, due to the integration of forms the generic system. The error rates the name component into the general text analysis compare favorably with results reported in system of GerTTS, the latter problem has a reason- the research literature. Finally, we discuss able solution. areas for future work. 2 Some problems in name analysis 1 Introduction What makes name pronunciation difficult, or spe- The correct pronunciation of names is one of the cial, in comparison to words that are considered as biggest challenges for text-to-speech (TTS) conver- regular entries in the lexicon of a given language? sion systems. At the same time, many current or en- Various reasons are given in the research literature visioned applications, such as reverse directory sys- (Carlson, GranstrSm, and LindstrSm, 1989; Macchi tems, automated operator services, catalog ordering and Spiegel, 1990; Vitale, 1991; van Coile, Leys, and or navigation systems, to name just a few, crucially Mortier, 1992; Coker, Church, and Liberman, 1990; depend upon an accurate and intelligible pronuncia- Belhoula, 1993): tion of names. Besides these specific applications, • Names can be of very diverse etymological ori- any kind of well-formed text input to a general- gin and can surface in another language without purpose TTS system is extremely likely to contain undergoing the slow linguistic process of assim- names, and the system has to be well equipped to ilation to the phonological system of the new process these names. This requirement was the main language. motivation to develop a name analysis and pronunci- ation component for the German version of the Bell • The number of distinct names tends to be very Labs multilingual text-to-speech system (GerTTS) large: For English, a typical unabridged colle- (M6bius et al., 1996). giate dictionary lists about 250,000 word types, Names are conventionally categorized into per- whereas a list of surnames compiled from an sonal names (first and surnames), geographical address database contains 1.5 million types (72 names (place, city and street names), and brand million tokens) (Coker, Church, and Liberman, names (organization, company and product names). 1990). It is reasonable to assume similar ratios 49 for German, although no precise numbers are Generally speaking, nothing ensures correct pro- currently available. nunciation better than a direct hit in a pronuncia- • There is no exhaustive list of names; and tion dictionary. However, for the reasons detailed in German and some related Germanic lan- above this approach is not feasible for names. In guages, street names in particular are usu- short, we are not dealing with a memory or storage ally constructed like compounds (Rheins~ra~e, problem but with the requirement to be able to ap- Kennedyallee) which makes decomposition both proximately correctly analyze unseen orthographic practical and necessary. strings. We therefore decided to use a weighted finite-state transducer machinery, which is the tech- • Name pronunciation is known to be idiosyn- nological framework for the text analysis compo- cratic; there are many pronunciations contra- nents of the Bell Labs multilingual TTS system. dicting common phonological patterns, as FST technology enables the dynamic combination well as alternative pronunciations for certain and recombination of lexical and morphological sub- grapheme strings. strings, which cannot be achieved by a static pronun- • In many languages, general-purpose grapheme- ciation dictionary. We will now describe the proce- to-phoneme rules are to a significant extent dure of collecting lexically or morphologically mean- inappropriate for names (Macchi and Spiegel, ingful graphemic substrings that are used produc- 1990; Vitale, 1991). tively in name formation. • Names are not equally amenable to morpho- logical processes, such as word formation and 3 Productive name components derivation or to morphological decomposition, 3.1 Database as regular words are. That does not render such an approach unfeasible, though, as we show in Our training material is based on publically avail- this paper. able data extracted from a phone and address di- rectory of Germany. The database is provided on • The large number of different names together CD-ROM (D-Info, 1995). It lists all customers of with a restricted morphological structure leads Deutsche Telekom by name, street address, city, to a coverage problem: It is known that a rel- phone number, and postal code. The CD-l~OM atively small number of high-frequency words contains data retrieval and export software. The can cover a high percentage of word tokens in database is somewhat inconsistent in that informa- arbitrary text; the ratio is far less favorable tion for some fields is occasionally missing, more for names (Carlson, GranstrSm, and LindstrSm, than one person is listed in the name field, busi- 1989; van Coile, Leys, and Mortier, 1992). ness information is added to the name field, first We will now illustrate some of the idiosyncra- names and street names are abbreviated. Yet, due to cies and peculiarities of names that the analysis has its listing of more than 30 million customer records to cope with. Let us first consider morphological it provides an exhaustive coverage of name-related issues. Some German street names can be mor- phenomena in German. phologically and lexically analyzed, such as Kur- fiivst-en-damm ('electorial prince dam'), Kirche-n- 3.2 City names weg ('church path'). Many, however, are not de- The data retrieval software did not provide a way composable, such as Henmerich ('?') or Rimpar- to export a complete list of cities, towns, and vil- stra~e ('?Rimpar street'), at least not beyond ob- lages; thus we searched for all records listing city vious and unproblematic components (Stra~e, Weg, halls, township and municipality administrations Platz, etc.). and the like, and then exported the pertinent city Even more serious problems arise on the phono- names. This method yielded 3,837 city names, ap- logical level. As indicated above, general-purpose proximately 15% of all the cities (including urban pronunciation rules often do not apply to names. districts) covered in the database. It is reasonable For instance, the grapheme
Table 1: Extraction of productive street name components: quantitative data.
3.3 First names city, and so on. The training corpus for first names and street names Table 1 gives the numbers corresponding to the was assembled based on data from the four largest steps of the procedure just described. The number cities in Germany: Berlin, Hamburg, KJln (Cologne) of morphemes collected from the four cities is 1,940. and Miinchen (Munich). These four cities also pro- The selection criterion was frequency: Component vide an approximately representative geographical types occurring repeatedly within a city database and regional/dialectal coverage. The size and ge- were considered as productive or marginally produc- ography criteria were also applied to the selection of tive. The 1,940 morphemes recall 11,241 component the test material which was extracted from the cities types out of the total of 26,841 (or 41.9%), leaving of Frankfurt am Main and Dresden (see Evaluation). 15,600 types (or 58.1%) that are unaccounted for We retrieved all available first names from the ('residuals') by the morphemes. records of the four cities and collected those whose Residuals that occur in at least two out of four frequency exceeded 100. To this corpus we added the cities (2,008) were then added to the list of 1,940 most popular male and female (10 each) names given morphemes. The reasoning behind this is that there to newborn children in the years 1995/96, in both are component types that occur exactly once in a the former East and West Germany, according to an given city but do occur in virtually every city. To official statistical source on the internet. The cor- give a concrete example: There is usually only one pus also contains interesting spelling variants (Hel- Hauptstrafle ('main street') in any given city but you mut/Hellmuth) as well as peculiarities attributable almost certainly do find a Hauptstrafle in every city. to regional tastes and fashions (Maik, Maia). The After some editing and data clean-up, the final list total number of first names in our list is 754. of linguistically motivated street name morphemes contained 3,124 entries. No attempt was made to arrive at some form of morphological decomposition despite several obvious recurring components, such as <-hild>, <-bert>, 4 Compositional model of street <-fried>; the number of these components is very names small, and they are not productive in name-forming In this section we will present a compositional model processes anymore. of street names that is based on a morphological 3.4 Streets word model and also includes a phonetic syllable model. We will also describe the implementation of We retrieved all available street names from the these models in the form of a finite-state transducer. records of the four cities. The street names were split up into their individual word-like components, 4.1 Naming schemes for streets in German i.e., a street name like Konrad-Adenauer-Platz cre- Evidently, there is a finite list of lexical items ated three separate entries: Konrad, Adenauer, and that almost unambiguously mark a name as a Platz. This list was then sorted and made unique. street name; among these items are Strafle, Weg, The type inventory of street name components Platz, Gasse, Allee, Markt and probably a dozen was then used to collect lexically and semantically more. These street name markers are used to meaningful components, which we will henceforth construct street names involving persons (Stephan- conveniently call 'morphemes'. In analogy to the Lochner-Strafle, Kennedyallee), geographical places procedure for city names, these morphemes were (Tiibinger Allee), or objects (Chrysanthemenweg, used in a recall test on the original street name com- Containerbahnho]); street names with local, regional ponent type list. This approach was successively ap- or dialectal peculiarities (Sb'bendieken, HJglstieg); plied to the street name inventory of the four cities, and finally intransparent street names (Kriisistrafle, starting with Mfinchen, exploiting the result of this Damaschkestrafle). Some names of the latter type first round in the second city, Berlin, applying the may actually refer to persons' names but the origin combined result of this second round on the third is not transparent to the native speaker. 51 START ROOT {Eps} also applied to the morphological analysis of com- ROOT FIRST SyllModel pounds and unknown words. Figure 1 shows parts of the arclist source file for ROOT FIRST''Al~ons{firstname}<0.2> street name decomposition. The arc which describes ROOT FIRST D'irk{firstname}<0.2> the transition from the initial state "START" to the ROOT FIRST D'ominik<{firstname}0.2> state "ROOT" is labeled with ¢ (Epsilon, the empty • • • • string). The transition from "ROOT" to the state ROOT FIRST b'urg{city} "FIRST" is defined by three large families of arcs ROOT FIRST br'uck{city}<0.2> which represent the lists of first names, productive ROOT FIRST d'orf{city} ROOT FIRST fl'eet{city}<0.2> city name components, and productive street name components, respectively, as described in the previ- ROOT FiRST'd'ach{street}<0.2> ous section. ROOT FIRST h'ecke{street}<0.2> The transition from "ROOT" to "FIRST" which ROOT FIRST kl'ar{street}<0.2> is labeled SyllModel is a place holder for a pho- ROOT FIRST kl'ee{street}<0.2> netic syllable model. This syllable model reflects ROOT FIRST kl'ein{street}<0.2> the phonotactics and the segmental structure of syl- ROOT FIRST st'ein{street}<0.2> lables in German, or rather their correlates on the orthographic surface. This allows the module to an- ROOT FiRST'all~ee{marker} alyze substrings of names that are unaccounted for ROOT FIRST g'arten{marker} ROOT FIRST pl'atz{marker} by the explicitly listed name components (see 'resid- ROOT FIRST w'eg{marker} uals' in the previous section) in arbitrary locations in a complex name. A detailed discussion of the syl- FIRST R00T'{++}<0.1> lable model is presented elsewhere (MSbius, 1997). FIRST FUGE {Eps}<0.2> From the state "FIRST" there is a transition FIRST FUGE s<0.2> back to "ROOT", either directly or via the state FIRST FUGE n<0.2> "FUGE', thereby allowing arbitrarily long con- FIRST SUFFIX {Eps}<0.2> catenations of name components. Labels on the FIRST SUFFIX s<0.2> arcs to "FUGE" represent infixes ('Fugen') that FIRST SUFFIX n<0.2> FUGE FIRST {Eps}
++/0.5
Figure 2: The transducer compiled from the sub-grammar that performs the decomposition of the fictitious street name Dachsteinhohenheckenalleenplatz. duced as street name markers, making them more of the last component, platz, is zero because this is likely to be used during name decomposition. The one of the customary street name markers. Finally, orthographic strings are annotated with symbols for the completely analyzed word is tagged as a name, primary (') and secondary (") lexical stress. The and a word boundary is appended on the way to the symbol {++} indicates a morpheme boundary. final state "END". The finite-state transducer that this grammar is The morphological information provided by the compiled into is far too complex to be usefully dia- name analysis component is exploited by the phono- grammed here. For the sake of exemplification, let us logical or pronunciation rules. This component of instead consider the complex fictitious street name the linguistic analysis is implemented using a mod- Dachsteinhohenheckenalleenplatz. Figure 2 shows ified version of the Kaplan and Kay rewrite rule al- the transducer corresponding to the sub-grammar gorithm (Kaplan and Kay, 1994). that performs the decomposition of this name. The path through the graph is as follows: 5 Evaluation The arc between the initial state "START" and "ROOT" is labeled with a word boundary {##} We evaluated the name analysis system by compar- and zero cost (0). From here we take the arc with ing the pronunciation performance of two versions the label d'ach and a cost of 0.2 to state "FIRST". of the TTS system, one with and one without the The next name component that can be found in the name-specific module. We ran both versions on two grammar is stein; we have to return to "ROOT" by lists of street names, one selected from the training way of an arc that is labeled with a morph bound- material and the other from unseen data. ary and a cost of 0.1. The next known component is hecke, leaving a residual string hohen which has to be 5.1 General-purpose vs. name-specific analyzed by means of the syllable model. Applying analysis the syllable model is expensive because we want to cover the name string with as many known compo- Two versions of the German TTS system were in- nents as possible. The costs actually vary depending volved in the evaluation experiments, differing in upon the number of syllables in the residual string the structure of the text analysis component. The and the number of graphemes in each syllable; the first system contained the regular text analysis mod- string hohen would thus have be decomposed into a ules, including a general-purpose module that han- root hohe and the 'Fuge' n. For the sake of simplic- dles words that are not represented in the system's ity we assign a flat cost of 10.0 in our toy example. lexicon: typically compounds and names. This ver- In the transition between hecke and allee a 'Fuge' sion will be refered to as the old system. The second (n) has to be inserted. The cost of the following version purely consisted of the name grammar trans- morph boundary is higher (0.5) than usual in order ducer discussed in the previous section. It did not to favor components that do not require infixation. have any other lexical information at its disposal. Another Fuge has to be inserted after allee. The cost This version will be refered to as the new system. 53 Training Data Test Data number of names 631 206 at least one system wrong 250/631 (39.6%) 82/206 (39.8%) both systems wrong 72/250 (28.8%) 26/82 (31.7%) total error rate (no correct solution) 72/631 (11.1%) 26/206 (12.6%)
Table 2: Performance of the general-purpose and the name-specific text analysis systems on training and test data sets.
H Training Data Test Data ] new system correct && old system wrong 138/163 (84.7%) 35/50 (70.0%) old system correct && new system wrong 25/163 (15.3%) 15/50 (30.0%) net improvement II 113/163
Table 3: Comparison between the general-purpose and the name-specific text analysis systems on training and test data sets.
5.2 Training vs. test materials out of these 250 cases (28.8%) both systems were The textual materials used in the evaluation exper- wrong. Thus, for 72 out of 631 names (11.4%) no iments consisted of two sets of data. The first set, correct transcription was obtained by either system. henceforth training data, was a subset of the data On the test data, at least one of the two sys- that were used in building the name analysis gram- tems was incorrect in 82 out of a total of 206 names mar. For this set, the street names for each of the (39.8%), an almost identical result as for the training four cities Berlin, Hamburg, KSln and Miinchen were data. However, in 26 out of these 82 cases (31.7%) randomized. We then selected every 50th entry from both systems were wrong. In other words, no cor- the four files, yielding a total of 631 street names; rect transcription was obtained by either system for thus, the training set also reflected the respective 26 out of 206 names (12.6%), which is only slightly size of the cities. higher than for the training data. The second set, henceforth test data, was ex- Table 3 compares the performances of the two tracted from the databases of the cities Frankfurt am text analysis systems. On the training data, the Main and Dresden. Using the procedure described new system outperforms the old one in 138 of the above, we selected 206 street names. Besides be- 163 cases (84.7%) where one of the systems was cor- ing among the ten largest German cities, Frankfurt rect and the other one was wrong; we disregard here and Dresden also meet the requirement of a bal- all cases where both systems were correct as well anced geographical and dialectal coverage. These as the 87 names for which no correct transcription data were not used in building the name analysis was given by either system. But there were also 25 system. cases (15.3%) where the old system outperformed the new one. Thus, the net improvement by the 5.3 Results name-specific system over the old one is 69.4%. The old and the new versions of the TTS system On the test data set, the old system gives the cor- were run on the training and the test set. Pronun- rect solution in 15 of 50 cases (30.0%), compared to ciation performance was evaluated on the symbolic 35 names (70.0%) for which the new system gives the level by manually checking the correctness of the re- correct transcription; again, all cases were excluded sulting transcriptions. A transcription was consid- in which both systems performed equally well or ered correct when no segmental errors or erroneous poorly. The net improvement by the name-specific syllabic stress assignments were detected. Multiple system over the generic one on the test data is thus mistakes within the same name were considered as 40%. one error. Thus, we made a binary decision between A detailed error analysis yielded the following correct and incorrect transcriptions. types of remaining problems: Table 2 summarizes the results. On the training data, in 250 out of a total of 631 names (39.6%) at • Syllabic stress: Saarbriicken [za:~bR'Yk~n] but least one of the two systems was incorrect. In 72 Zweibriicken [tsv'aibRYk~n]. 54 • Vowel quality: S'oest [zo:st], not [zO:st] or names that were manually transcribed by an expert [zo:ost]. phonetician. However, the Onomastica results can only serve • Consonant quality: Chemnitz [kcmnits], not as a qualitative point of reference and should not [~e:mnits] in analogy to chemisch [~e:mIf]. be compared to our results in a strictly quantitative • Morphology: Erroneous decomposition of sub- sense, for the following reasons. First, the percent- strings (hyper-correction over old system); e.g., age of proper names is likely to be much higher in Rim+par+strafle [ri:mpa~] instead of Rim- the Onomastica database (no numbers are given in par+strafle [rimpa~]. the report), in which ease higher error rates should be expected due to the inherent difficulty of proper • Pronunciation rules: "Holes" in the general- name pronunciation. In our study, proper names purpose pronunciation rule set were revealed by were only covered in the context of street names. orthographic substrings that do not occur in the Second, Onomastica did not apply morphological regular lexicon. It has been shown for English analysis to names, while morphological decomposi- (van Santen, 1992) that the frequency distribu- tion, and word and syllable models, are the core of tion of triphones in names is quite dissimilar to our approach. Third, Onomastica developed name- the one found in regular words. specific grapheme-to-phoneme rule sets, whereas we • Idiosyncrasies: Peculiar pronunciations that did not augment the general-purpose pronunciation cannot be described by rules and that even na- rules. tive speakers quite often do not know or do How can the remaining problems be solved, and not agree upon; e.g., Oeynhausen [¢~:nhauzon], what are the topics for future work? For the Duisdorf [dy:sd~f] or [du:sd~f] or [du:isd~f]. task of grapheme-to-phoneme conversion, several ap- proaches have been proposed as alternatives to ex- 6 Discussion and future work plicit rule systems, particularly self-learning meth- ods (van Coile, 1990; Torkkola, 1993; Andersen and After the evaluation, the name analysis transducer Dalsgaard, 1994) and neural networks (Sejnowski was integrated into the text analysis component of and Rosenberg, 1987; An et al., 1988). None of these the German TTS system. The weights were adjusted methods were explored and applied in the present in such a way that for any token, i.e., word or word study. One reason is that it is difficult to construct form, in the input text an immediate match in the or select a database if the set of factors that in- lexicon is always favored over name analysis which fluence name pronunciation is at least partially un- in turn is prefered to unknown word analysis. Even known. In addition, even for an initially incomplete though the evaluation experiments reported in this factor set the corresponding feature space is likely to paper were performed on names in isolation rather cause coverage problems; neural nets, for instance, than in sentential contexts, the error rates obtained are known to perform rather poorly at predicting in these experiments (Table 2) correspond to the per- unseen feature vectors. However, with the results of formance on names by the integrated text analysis the error analysis as a starting point, we feel that a component for arbitrary text. definition of the factor set is now more feasible. There are two ways of interpreting the results. On One obvious area for improvement is to add the one hand, despite a significant improvement over a name-specific set of pronunciation rules to the the previous general-purpose text analysis we have general-purpose one. Using this approach, Belhoula to expect a pronunciation error rate of 11-13% for (Belhoula, 1993) reports error rates of 4.3% for Ger- unknown names. In other words, roughly one out of man place names and 10% for last names. These re- eight names will be pronounced incorrectly. sults are obtained in recall tests on a manually tran- On the other hand, this performance compares scribed training corpus; it remains unclear, however, rather favorably with the results reported for the whether the error rates are reported by letter or by German branch of the European Onomastica project word. (Onomastica, 1995). Onomastica was funded by the The addition of name-specific rules presupposes European Community from 1993 to 1995 and aimed that the system knows which orthographic strings to produce pronunciation dictionaries of proper are names and which are regular words. The prob- names and place names in eleven languages. The lem of name detection in arbitrary text (see (Thie- final report describes the performance of grapheme- lea, 1995) for an approach to German name tagging) to-phoneme rule sets developed for each language. has not been addressed in our study; instead, it was For German, the accuracy rate for quality band III-- by-passed for the time being by integrating the name names which were transcribed by rule only--was component into the general text analysis system and 71%; in other words, the error rate in the same sense by adjusting the weights appropriately. as used in this paper was 29%. The grapheme-to- Other areas for future work are the systematic phoneme conversion rules were written by experts, treatment of proper names outside the context of based on tens of thousands of the most frequent street names, and of brand names, trademarks, and 55 company names. One important consideration here Marian Macchi and Murray Spiegel. 1990. Us- is the recognition of the ethnic origin of a name and ing a demisyllable inventory to synthesize names. the application of appropriate specific pronunciation Speech Technology, pages 208-212. rules. Heuristics, such as name pronunciation by Bernd M6bius. 1997. Text analysis in the Bell Labs analogy and rhyming (Coker, Church, and Liber- German TTS system. Technical report, Bell Lab- man, 1990) and methods for, e.g., syllabic stress as- oratories. signment (Church, 1986) can serve as role models Bernd MSbius, Juergen Schroeter, Jan van Santen, for this ambitious task. Richard Sproat, and Joseph Olive. 1996. Recent advances in multilingual text-to-speech synthesis. 7 Acknowledgments In Fortschritte der Akustik--DA GA '96, Bad Hon- We wish to acknowledge Richard Sproat who devel- nef, Germany. DPG. oped and provided the lextools toolkit; this work also Onomastica. 1995. Multi-language pronunciation benefited from his advice. 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