A Finite State and Data-Oriented Method for Grapheme to Phoneme Conversion

A finite-state method, based on leftmost longest-match replacement, is presented for segmenting words into graphemes, and for converting graphemes into phonemes. A small set of hand-crafted conversion rules for Dutch achieves a phoneme accuracy of over 93%. The accuracy of the system is further improved by using transformation-based learning. The phoneme accuracy of the best system (using a large set of rule templates and a `lazy' variant of Brill's algoritm), trained on only 40K words, reaches 99% accuracy.Comment: 8 page

Meta-Learning for Phonemic Annotation of Corpora

We apply rule induction, classifier combination and meta-learning (stacked classifiers) to the problem of bootstrapping high accuracy automatic annotation of corpora with pronunciation information. The task we address in this paper consists of generating phonemic representations reflecting the Flemish and Dutch pronunciations of a word on the basis of its orthographic representation (which in turn is based on the actual speech recordings). We compare several possible approaches to achieve the text-to-pronunciation mapping task: memory-based learning, transformation-based learning, rule induction, maximum entropy modeling, combination of classifiers in stacked learning, and stacking of meta-learners. We are interested both in optimal accuracy and in obtaining insight into the linguistic regularities involved. As far as accuracy is concerned, an already high accuracy level (93% for Celex and 86% for Fonilex at word level) for single classifiers is boosted significantly with additional error reductions of 31% and 38% respectively using combination of classifiers, and a further 5% using combination of meta-learners, bringing overall word level accuracy to 96% for the Dutch variant and 92% for the Flemish variant. We also show that the application of machine learning methods indeed leads to increased insight into the linguistic regularities determining the variation between the two pronunciation variants studied.Comment: 8 page

Morphological Analysis as Classification: an Inductive-Learning Approach

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning algorithms are applied to three variations of the task of morphological analysis. Results show (i) that the generalisation performance of the algorithms is good, and (ii) that the lazy learning algorithm IB1-IG performs best on all three tasks. We conclude that lazy learning of morphological analysis as a classification task is indeed a viable approach; moreover, it has the strong advantages over the traditional approach of avoiding the knowledge-acquisition bottleneck, being fast and deterministic in learning and processing, and being language-independent.Comment: 11 pages, 5 encapsulated postscript figures, uses non-standard NeMLaP proceedings style nemlap.sty; inputs ipamacs (international phonetic alphabet) and epsf macro

Memory-Based Lexical Acquisition and Processing

Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described.Comment: 18 page

Nearest Neighbor-Based Indonesian G2P Conversion

Grapheme-to-phoneme conversion (G2P), also known as letter-to-sound conversion, is an important module in both speech synthesis and speech recognition. The methods of G2P give varying accuracies for different languages although they are designed to be language independent. This paper discusses a new model based on pseudo nearest neighbor rule (PNNR) for Indonesian G2P. In this model, partial orthogonal binary code for graphemes, contextual weighting, and neighborhood weighting are introduced. Testing to 9,604 unseen words shows that the model parameters are easy to be tuned to reach high accuracy. Testing to 123 sentences containing homographs shows that the model could disambiguate homographs if it uses long graphemic context. Compare to information gain tree, PNNR gives slightly higher phoneme error rate, but it could disambiguate homographs

A Comparison of Different Machine Transliteration Models

Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models -- grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model -- have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance

Introducing nativization to Spanish TTS systems

In the modern world, speech technologies must be flexible and adaptable to any framework. Mass media globalization introduces multilingualism as a challenge for the most popular speech applications such as text-to-speech synthesis and automatic speech recognition. Mixed-language texts vary in their nature and when processed, some essential characteristics must be considered. In Spain and other Spanish-speaking countries, the use of Anglicisms and other words of foreign origin is constantly growing. A particularity of peninsular Spanish is that there is a tendency to nativize the pronunciation of non-Spanish words so that they fit properly into Spanish phonetic patterns. In our previous work, we proposed to use hand-crafted nativization tables that were capable of nativizing correctly 24% of words from the test data. In this work, our goal was to approach the nativization challenge by data-driven methods, because they are transferable to other languages and do not drop in performance in comparison with explicit rules manually written by experts. Training and test corpora for nativization consisted of 1000 and 100 words respectively and were crafted manually. Different specifications of nativization by analogy and learning from errors focused on finding the best nativized pronunciation of foreign words. The best obtained objective nativization results showed an improvement from 24% to 64% in word accuracy in comparison to our previous work. Furthermore, a subjective evaluation of the synthesized speech allowed for the conclusion that nativization by analogy is clearly the preferred method among listeners of different backgrounds when comparing to previously proposed methods. These results were quite encouraging and proved that even a small training corpus is sufficient for achieving significant improvements in naturalness for English inclusions of variable length in Spanish utterances.Peer ReviewedPostprint (published version

Knowledge-light Letter-to-Sound Conversion for Swedish with FST and TBL

This paper describes some exploratory attempts to apply a combination of finite state transducers (FST) and transformation-based learning (TBL, Brill 1992) to the problem of letter-to-sound (LTS) conversion for Swedish. Following Bouma (2000) for Dutch, we employ FST for segmentation of the textual input into groups of letters and a first transcription stage; we feed the output of this step into a TBL system. With this setup, we reach 96.2% correctly transcribed segments with rather restricted means (a small set of hand-crafted rules for the FST stage; a set of 12 templates and a training set of 30kw for the TBL stage). Observing that quantity is the major error source and that compound morpheme boundaries can be useful for inferring quantity, we exploratively add good precision-low recall compound splitting based on graphotactic constraints. With this simple-minded method, targeting only a subset of the compounds, performance improves to 96.9%