Applying dynamic Bayesian networks in transliteration detection and generation

Peter Nabende promoveert op methoden die programma’s voor automatisch vertalen kunnen verbeteren. Hij onderzocht twee systemen voor het genereren en vergelijken van transcripties: een DBN-model (Dynamische Bayesiaanse Netwerken) waarin Pair Hidden Markovmodellen zijn geïmplementeerd en een DBN-model dat op transductie is gebaseerd. Nabende onderzocht het effect van verschillende DBN-parameters op de kwaliteit van de geproduceerde transcripties. Voor de evaluatie van de DBN-modellen gebruikte hij standaard dataverzamelingen van elf taalparen: Engels-Arabisch, Engels-Bengaals, Engels-Chinees, Engels-Duits, Engels-Frans, Engels-Hindi, Engels-Kannada, Engels-Nederlands, Engels-Russisch, Engels-Tamil en Engels-Thai. Tijdens het onderzoek probeerde hij om verschillende modellen te combineren. Dat bleek een goed resultaat op te leveren

Communicating Unknown Words in Machine Translation

A new approach to handle unknown words in machine translation is presented. The basic idea is to find definitions for the unknown words on the source language side and translate those definitions instead. Only monolingual resources are required, which generally offer a broader coverage than bilingual resources and are available for a large number of languages. In order to use this in a machine translation system definitions are extracted automatically from online dictionaries and encyclopedias. The translated definition is then inserted and clearly marked in the original hypothesis. This is shown to lead to significant improvements in (subjective) translation quality

A Baybayin word recognition system

Baybayin is a pre-Hispanic Philippine writing system used in Luzon island. With the effort in reintroducing the script, in 2018, the Committee on Basic Education and Culture of the Philippine Congress approved House Bill 1022 or the ”National Writing System Act,” which declares the Baybayin script as the Philippines’ national writing system. Since then, Baybayin OCR has become a field of research interest. Numerous works have proposed different techniques in recognizing Baybayin scripts. However, all those studies anchored on the classification and recognition at the character level. In this work, we propose an algorithm that provides the Latin transliteration of a Baybayin word in an image. The proposed system relies on a Baybayin character classifier generated using the Support Vector Machine (SVM). The method involves isolation of each Baybayin character, then classifying each character according to its equivalent syllable in Latin script, and finally concatenate each result to form the transliterated word. The system was tested using a novel dataset of Baybayin word images and achieved a competitive 97.9% recognition accuracy. Based on our review of the literature, this is the first work that recognizes Baybayin scripts at the word level. The proposed system can be used in automated transliterations of Baybayin texts transcribed in old books, tattoos, signage, graphic designs, and documents, among others

Conversational Arabic Automatic Speech Recognition

Colloquial Arabic (CA) is the set of spoken variants of modern Arabic that exist in the form of regional dialects and are considered generally to be mother-tongues in those regions. CA has limited textual resource because it exists only as a spoken language and without a standardised written form. Normally the modern standard Arabic (MSA) writing convention is employed that has limitations in phonetically representing CA. Without phonetic dictionaries the pronunciation of CA words is ambiguous, and can only be obtained through word and/or sentence context. Moreover, CA inherits the MSA complex word structure where words can be created from attaching affixes to a word. In automatic speech recognition (ASR), commonly used approaches to model acoustic, pronunciation and word variability are language independent. However, one can observe significant differences in performance between English and CA, with the latter yielding up to three times higher error rates. This thesis investigates the main issues for the under-performance of CA ASR systems. The work focuses on two directions: first, the impact of limited lexical coverage, and insufficient training data for written CA on language modelling is investigated; second, obtaining better models for the acoustics and pronunciations by learning to transfer between written and spoken forms. Several original contributions result from each direction. Using data-driven classes from decomposed text are shown to reduce out-of-vocabulary rate. A novel colloquialisation system to import additional data is introduced; automatic diacritisation to restore the missing short vowels was found to yield good performance; and a new acoustic set for describing CA was defined. Using the proposed methods improved the ASR performance in terms of word error rate in a CA conversational telephone speech ASR task