Bootstrapping techniques have the potential to accelerate the development of language technology resources. This is of specific importance in the developing world where language technology resources are scarce and linguistic diversity is high. In this thesis we analyse the pronunciation modelling task within a bootstrapping framework, as a case study in the bootstrapping of language technology resources. We analyse the grapheme-to-phoneme conversion task in the search for a grapheme-to-phoneme conversion algorithm that can be utilised during bootstrapping. We experiment with enhancements to the Dynamically Expanding Context algorithm and develop a new algorithm for grapheme-tophoneme rule extraction (Default & Refine) that utilises the concept of a ‘default phoneme’ to create a cascade of increasingly specialised rules. This algorithm displays a number of attractive properties including rapid learning, language independence, good asymptotic accuracy, robustness to noise, and the production of a compact rule set. In order to have greater flexibility with regard to the various heuristic choices made during rewrite rule extraction, we define a new theoretical framework for analysing instance-based learning of rewrite rule sets. We define the concept of minimal representation graphs, and discuss the utility of these graphs in obtaining the smallest possible rule set describing a given set of discrete training data. We develop an approach for the interactive creation of pronunciation models via bootstrapping, and implement this approach in a system that integrates various of the analysed grapheme-to-phoneme alignment and conversion algorithms. The focus of this work is on combining machine learning and human intervention in such a way as to minimise the amount of human effort required during bootstrapping, and a generic framework for the analysis of this process is defined. Practical tools that support the bootstrapping process are developed and the efficiency of the process is analysed from both a machine learning and a human factors perspective. We find that even linguistically untrained users can use the system to create electronic pronunciation dictionaries accurately, in a fraction of the time the traditional approach requires. We create new dictionaries in a number of languages (isiZulu, Afrikaans and Sepedi) and demonstrate the utility of these dictionaries by incorporating them in speech technology systems. / Thesis (PhD (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

<p>A text-to-speech system is a complex system consisting of several different modules such as grapheme-to-phoneme conversion, articulatory and prosodic modelling, voice modelling etc.</p><p>This dissertation is aimed at the creation of the initial part of a text-to-speech system, i.e. the grapheme-to-phoneme conversion, designed for Swedish. The problem area at hand is the conversion of orthographic text into a phonetic representation that can be used as a basis for a future complete text-to speech system.</p><p>The central issue of the dissertation is the grapheme-to-phoneme conversion and the elaboration of rules and algorithms required to achieve this task. The dissertation aims to prove that it is possible to make such a conversion by a rule-based algorithm with reasonable performance. Another goal is to find a way to represent phonotactic rules in a form suitable for parsing. It also aims to find and analyze problematic structures in written text compared to phonetic realization.</p><p>This work proposes a knowledge-based grapheme-to-phoneme conversion system for Swedish. The system suggested here is implemented, tested, evaluated and compared to other existing systems. The results achieved are promising, and show that the system is fast, with a high degree of accuracy.</p>

A text-to-speech system is a complex system consisting of several different modules such as grapheme-to-phoneme conversion, articulatory and prosodic modelling, voice modelling etc. This dissertation is aimed at the creation of the initial part of a text-to-speech system, i.e. the grapheme-to-phoneme conversion, designed for Swedish. The problem area at hand is the conversion of orthographic text into a phonetic representation that can be used as a basis for a future complete text-to speech system. The central issue of the dissertation is the grapheme-to-phoneme conversion and the elaboration of rules and algorithms required to achieve this task. The dissertation aims to prove that it is possible to make such a conversion by a rule-based algorithm with reasonable performance. Another goal is to find a way to represent phonotactic rules in a form suitable for parsing. It also aims to find and analyze problematic structures in written text compared to phonetic realization. This work proposes a knowledge-based grapheme-to-phoneme conversion system for Swedish. The system suggested here is implemented, tested, evaluated and compared to other existing systems. The results achieved are promising, and show that the system is fast, with a high degree of accuracy.

No description available.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: This thesis investigates various data-driven techniques by which pronunciation dictionaries can be automatically augmented. First, well-established grapheme-to-phoneme (G2P) conversion techniques are evaluated for Standard South African English (SSAE), British English (RP) and American English (GenAm) by means of four appropriate dictionaries: SAEDICT, BEEP, CMUDICT and PRONLEX. Next, the decision tree algorithm is extended to allow the conversion of pronunciations between different accents by means of phoneme-to-phoneme (P2P) and grapheme-andphoneme- to-phoneme (GP2P) conversion. P2P conversion uses the phonemes of the source accent as input to the decision trees. GP2P conversion further incorporates the graphemes into the decision tree input. Both P2P and GP2P conversion are evaluated using the four dictionaries. It is found that, when the pronunciation is needed for a word not present in the target accent, it is substantially more accurate to modify an existing pronunciation from a different accent, than to derive it from the word’s spelling using G2P conversion. When converting between accents, GP2P conversion provides a significant further increase in performance above P2P. Finally, experiments are performed to determine how large a training dictionary is required in a target accent for G2P, P2P and GP2P conversion. It is found that GP2P conversion requires less training data than P2P and substantially less than G2P conversion. Furthermore, it is found that very little training data is needed for GP2P to perform at almost maximum accuracy. The bulk of the accuracy is achieved within the initial 500 words, and after 3000 words there is almost no further improvement. Some specific approaches to compiling the best training set are also considered. By means of an iterative greedy algorithm an optimal ranking of words to be included in the training set is discovered. Using this set is shown to lead to substantially better GP2P performance for the same training set size in comparison with alternative approaches such as the use of phonetically rich words or random selections. A mere 25 words of training data from this optimal set already achieve an accuracy within 1% of that of the full training dictionary. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek verskeie data-gedrewe tegnieke waarmee uitspraakwoordeboeke outomaties aangevul kan word. Eerstens word gevestigde grafeem-na-foneem (G2P) omskakelingstegnieke ge¨evalueer vir Standaard Suid-Afrikaanse Engels (SSAE), Britse Engels (RP) en Amerikaanse Engels (GenAm) deur middel van vier geskikte woordeboeke: SAEDICT, BEEP, CMUDICT en PRONLEX. Voorts word die beslissingsboomalgoritme uitgebrei om die omskakeling van uitsprake tussen verskillende aksente moontlik te maak, deur middel van foneem-na-foneem (P2P) en grafeem-en-foneem-na-foneem (GP2P) omskakeling. P2P omskakeling gebruik die foneme van die bronaksent as inset vir die beslissingsbome. GP2P omskakeling inkorporeer verder die grafeme by die inset. Beide P2P en GP2P omskakeling word evalueer deur middel van die vier woordeboeke. Daar word bevind dat wanneer die uitspraak benodig word vir ’n woord wat nie in die teikenaksent teenwoordig is nie, dit bepaald meer akkuraat is om ’n bestaande uitspraak van ’n ander aksent aan te pas, as om dit af te lei vanuit die woord se spelling met G2P omskakeling. Wanneer daar tussen aksente omgeskakel word, gee GP2P omskakeling ’n verdere beduidende verbetering in akkuraatheid bo P2P. Laastens word eksperimente uitgevoer om die grootte te bepaal van die afrigtingswoordeboek wat benodig word in ’n teikenaksent vir G2P, P2P en GP2P omskakeling. Daar word bevind dat GP2P omskakeling minder afrigtingsdata as P2P en substansieel minder as G2P benodig. Verder word dit bevind dat baie min afrigtingsdata benodig word vir GP2P om teen bykans maksimum akkuraatheid te funksioneer. Die oorwig van die akkuraatheid word binne die eerste 500 woorde bereik, en n´a 3000 woorde is daar amper geen verdere verbetering nie. ’n Aantal spesifieke benaderings word ook oorweeg om die beste afrigtingstel saam te stel. Deur middel van ’n iteratiewe, gulsige algoritme word ’n optimale rangskikking van woorde bepaal vir insluiting by die afrigtingstel. Daar word getoon dat deur hierdie stel te gebruik, substansieel beter GP2P gedrag verkry word vir dieselfde grootte afrigtingstel in vergelyking met alternatiewe benaderings soos die gebruik van foneties-ryke woorde of lukrake seleksies. ’n Skamele 25 woorde uit hierdie optimale stel gee reeds ’n akkuraatheid binne 1% van di´e van die volle afrigtingswoordeboek.

No description available.

Text -to-speech systems currently are of great importance in the community. One core technology in this human language technology resource is stress assignment which plays an important role in any text-to-speech system. At present no automatic stress assigner for Afrikaans exists. For these reasons, the two most important aims of this project will be: a) to develop a complete and accurate set of stress rules for Afrikaans that can be implemented in an automatic stress assigner, and b) to develop an effective and highly accurate stress assigner in order to assign Afrikaans stress to words quickly and effectively. A set of stress rules for Afrikaans was developed in order to reach the first goal. It consists of 18 rules that are divided into groups for words that contain a schwa, derivations, and disyllabic, tri-syllabic and polysyllabic simplex words. Next, different approaches that can be used to develop a stress assigner were examined, and the rule-based approach was used to implement the developed stress rules within the stress assigner. The programming language, Perl, was chosen for the implementation of the rules. The chosen algorithm was used to generate a stress assigner for Afrikaans by implementing the stress rules developed. The hyphenator, Calomo and the compound analyser, CKarma was used to hyphenate all the test data and detect word boundaries within compounds. A dataset of 10 000 correctly annotated tokens was developed during the testing process. The evaluation of the stress assigner consists of four phases. During the first phase, the stress assigner was evaluated with the 10 000 tokens and achieved an accuracy of 92.09%. The grapheme - to-phoneme converter was evaluated with the same data and scored 91.9%. The influence of various factors on stress assignment was determined, and it was established that stress assignment is an essential component of rule-based grapheme-to-phoneme conversion. In conclusion, it can be said that the stress assigner achieved satisfactory results, and that the stress assigner can be successfully utilized in future projects to develop training data for further experiments with stress assignment and grapheme-to-phoneme conversion for Afrikaans. Experiments can be conducted in future with data-driven approaches that possibly may lead to better results in Afrikaans stress assignment and grapheme-to-phoneme conversion. / Thesis (M.A. (Applied Language and Literary Studies))--North-West University, Potchefstroom Campus, 2010.

Text -to-speech systems currently are of great importance in the community. One core technology in this human language technology resource is stress assignment which plays an important role in any text-to-speech system. At present no automatic stress assigner for Afrikaans exists. For these reasons, the two most important aims of this project will be: a) to develop a complete and accurate set of stress rules for Afrikaans that can be implemented in an automatic stress assigner, and b) to develop an effective and highly accurate stress assigner in order to assign Afrikaans stress to words quickly and effectively. A set of stress rules for Afrikaans was developed in order to reach the first goal. It consists of 18 rules that are divided into groups for words that contain a schwa, derivations, and disyllabic, tri-syllabic and polysyllabic simplex words. Next, different approaches that can be used to develop a stress assigner were examined, and the rule-based approach was used to implement the developed stress rules within the stress assigner. The programming language, Perl, was chosen for the implementation of the rules. The chosen algorithm was used to generate a stress assigner for Afrikaans by implementing the stress rules developed. The hyphenator, Calomo and the compound analyser, CKarma was used to hyphenate all the test data and detect word boundaries within compounds. A dataset of 10 000 correctly annotated tokens was developed during the testing process. The evaluation of the stress assigner consists of four phases. During the first phase, the stress assigner was evaluated with the 10 000 tokens and achieved an accuracy of 92.09%. The grapheme - to-phoneme converter was evaluated with the same data and scored 91.9%. The influence of various factors on stress assignment was determined, and it was established that stress assignment is an essential component of rule-based grapheme-to-phoneme conversion. In conclusion, it can be said that the stress assigner achieved satisfactory results, and that the stress assigner can be successfully utilized in future projects to develop training data for further experiments with stress assignment and grapheme-to-phoneme conversion for Afrikaans. Experiments can be conducted in future with data-driven approaches that possibly may lead to better results in Afrikaans stress assignment and grapheme-to-phoneme conversion. / Thesis (M.A. (Applied Language and Literary Studies))--North-West University, Potchefstroom Campus, 2010.

Grapheme-to-phoneme conversion (G2P) is the task of converting a word, represented by a sequence of graphemes, to its pronunciation, represented by a sequence of phonemes. The G2P task plays a crucial role in speech synthesis systems, and is an important part of other applications, including spelling correction and speech-to-speech machine translation. G2P conversion is a complex task, for which a number of diverse solutions have been proposed. In general, the problem is challenging because the source string does not unambiguously specify the target representation. In addition, the training data include only example word pairs without the structural information of subword alignments. In this thesis, I introduce several novel approaches for G2P conversion. My contributions can be categorized into (1) new alignment models and (2) new output generation models. With respect to alignment models, I present techniques including many-to-many alignment, phonetic-based alignment, alignment by integer linear programing and alignment-by-aggregation. Many-to-many alignment is designed to replace the one-to-one alignment that has been used almost exclusively in the past. The new many-to-many alignments are more precise and accurate in expressing grapheme-phoneme relationships. The other proposed alignment approaches attempt to advance the training method beyond the use of Expectation-Maximization (EM). With respect to generation models, I first describe a framework for integrating many-to-many alignments and language models for grapheme classification. I then propose joint processing for G2P using online discriminative training. I integrate a generative joint n-gram model into the discriminative framework. Finally, I apply the proposed G2P systems to name transliteration generation and mining tasks. Experiments show that the proposed system achieves state-of-the-art performance in both the G2P and name transliteration tasks.

No description available.