Classifying Graphemes in English Words Through the Application of a Fuzzy Inference System
In Linguistics, a grapheme is a written unit of a writing system corresponding to a phonological sound. In Natural Language Processing tasks, written language is analysed through two different mediums, word analysis, and character analysis. This paper focuses on a third approach, the analysis of graphemes. Graphemes have advantages over word and character analysis by being self-contained representations of phonetic sounds. Due to the nature of splitting a word into graphemes being based on complex, non-binary rules, the application of fuzzy logic would provide a suitable medium upon which to predict the number of graphemes in a word. This paper proposes the application of a Fuzzy Inference System to split words into their graphemes. This Fuzzy Inference System results in a correct prediction of the number of graphemes in a word 50.18% of the time, with 93.51% being within a margin of +- 1 from the correct classification. Given the variety in language, graphemes are tied with pronunciation and therefore can change depending on a regional accent/dialect, the +- 1 accuracy represents the impreciseness of grapheme classification when regional variances are accounted for. To give a baseline of comparison, a second method involving a recursive IPA mapping exercise using a pronunciation dictionary was developed to allow for comparisons to be made.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acou…
Language ModelingLanguage ModellingFrom Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially o…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Proper Nounspeech-recognition+1A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes
Latin has historically led the state-of-the-art in handwritten optical character recognition (OCR) research. Adapting existing systems from Latin to alpha-syllabary languages is particularly challenging due to a sharp co…
Multi-Label ClassificationOptical Character RecognitionOptical Character Recognition (OCR)Unicode Normalization and Grapheme Parsing of Indic Languages
Writing systems of Indic languages have orthographic syllables, also known as complex graphemes, as unique horizontal units. A prominent feature of these languages is these complex grapheme units that comprise consonants…
Language ModellingBabyLM's First Words: Word Segmentation as a Phonological Probing Task
Language models provide a key framework for studying linguistic theories based on prediction, but phonological analysis using large language models (LLMs) is difficult; there are few phonological benchmarks beyond Englis…