The pronunciation dictionary is a key resource required during the development of an automatic speech recognition (ASR) system. In this thesis, we adapt a British English pronunciation dictionary to Standard South African English (SSAE), as a case study in dialect adaptation. Our investigation leads us in three different directions: dictionary verification, phoneme redundancy evaluation and phoneme adaptation. A pronunciation dictionary should be verified for correctness before its implementation in experiments or applications. However, employing a human to verify a full pronunciation dictionary is an indulgent process which cannot always be accommodated. In our dictionary verification research we attempt to reduce the human effort required in the verification of a pronunciation dictionary by implementing automatic and semi-automatic techniques that find and isolate possible erroneous entries in the dictionary. We identify a number of new techniques that are very efficient in identifying errors, and apply them to a public domain British English pronunciation dictionary. Investigating phoneme redundancy involves looking into the possibility that not all phoneme distinctions are required in SSAE, and investigating different methods of analysing these distinctions. The methods that are investigated include both data driven and knowledge based pronunciation suggestions for a pronunciation dictionary used in an automatic speech recognition (ASR) system. This investigation facilitates a deeper linguistic insight into the pronunciation of phonemes in SSAE. Finally, we investigate phoneme adaptation by adapting the KIT phoneme between two dialects of English through the implementation of a set of adaptation rules. Adaptation rules are extracted from literature but also formulated through an investigation of the linguistic phenomena in the data. We achieve a 93% predictive accuracy, which is significantly higher than the 71 % achievable through the implementation of previously identified rules. The adaptation of a British pronunciation dictionary to SSAE represents the final step of developing a SSAE pronunciation dictionary, which is the aim of this thesis. In addition, an ASR system utilising the dictionary is developed, achieving an unconstrained phoneme accuracy of 79.7%. / Thesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.

The pronunciation dictionary is a key resource required during the development of an automatic speech recognition (ASR) system. In this thesis, we adapt a British English pronunciation dictionary to Standard South African English (SSAE), as a case study in dialect adaptation. Our investigation leads us in three different directions: dictionary verification, phoneme redundancy evaluation and phoneme adaptation. A pronunciation dictionary should be verified for correctness before its implementation in experiments or applications. However, employing a human to verify a full pronunciation dictionary is an indulgent process which cannot always be accommodated. In our dictionary verification research we attempt to reduce the human effort required in the verification of a pronunciation dictionary by implementing automatic and semi-automatic techniques that find and isolate possible erroneous entries in the dictionary. We identify a number of new techniques that are very efficient in identifying errors, and apply them to a public domain British English pronunciation dictionary. Investigating phoneme redundancy involves looking into the possibility that not all phoneme distinctions are required in SSAE, and investigating different methods of analysing these distinctions. The methods that are investigated include both data driven and knowledge based pronunciation suggestions for a pronunciation dictionary used in an automatic speech recognition (ASR) system. This investigation facilitates a deeper linguistic insight into the pronunciation of phonemes in SSAE. Finally, we investigate phoneme adaptation by adapting the KIT phoneme between two dialects of English through the implementation of a set of adaptation rules. Adaptation rules are extracted from literature but also formulated through an investigation of the linguistic phenomena in the data. We achieve a 93% predictive accuracy, which is significantly higher than the 71 % achievable through the implementation of previously identified rules. The adaptation of a British pronunciation dictionary to SSAE represents the final step of developing a SSAE pronunciation dictionary, which is the aim of this thesis. In addition, an ASR system utilising the dictionary is developed, achieving an unconstrained phoneme accuracy of 79.7%. / Thesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.

Analytic knowledge about the systematic variation in a language has an important place in the description of the language. Such knowledge is interesting e.g. in the language teaching domain, as a background for various types of linguistic studies, and in the development of more dynamic speech technology applications. In previous studies, the effects of single variables or relatively small groups of related variables on the pronunciation of words have been studied separately. The work described in this thesis takes a holistic perspective on pronunciation variation and focuses on a method for creating general descriptions of phone-level pronunciation in discourse context. The discourse context is defined by a large set of linguistic attributes ranging from high-level variables such as speaking style, down to the articulatory feature level. Models of phone-level pronunciation in the context of a discourse have been created for the central standard Swedish language variety. The models are represented in the form of decision trees, which are readable for both machines and humans. A data-driven approach was taken for the pronunciation modelling task, and the work involved the annotation of recorded speech with linguistic and related information. The decision tree models were induced from the annotation. An important part of the work on pronunciation modelling was also the development of a pronunciation lexicon for Swedish. In a cross-validation experiment, several sets of pronunciation models were created with access to different parts of the attributes in the annotation. The prediction accuracy of pronunciation models could be improved by 42.2% by making information from layers above the phoneme level accessible during model training. Optimal models were obtained when attributes from all layers of annotation were used. The goal for the models was to produce pronunciation representations representative for the language variety and not necessarily for the individual speakers, on whose speech the models were trained. In the cross-validation experiment, model-produced phone strings were compared to key phonetic transcripts of actual speech, and the phone error rate was defined as the share of discrepancies between the respective phone strings. Thus, the phone error rate is the sum of actual errors and discrepancies resulting from desired adaptations from a speaker-specific pronunciation to a pronunciation reflecting general traits of the language variety. The optimal models gave an average phone error rate of 8.2%. / QC 20100901