A Rule-Based Grapheme-to-Phoneme Conversion System

Kłosowski, Piotr

doi:10.3390/app12052758

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…The variability of Korean phonetic rules poses challenges for the knowledge-driven model, impacting conversion accuracy. Piotr K losowski [4] devel-oped a graphemeto-phoneme algorithm for Polish, but it exhibits inaccuracies with irregular words. Conflicting rules and neglecting pronunciation variations across speech databases demand substantial manual effort for constructing speech synthesis systems [5].…”

Section: Knowledge-driven G2p Approachmentioning

confidence: 99%

WITHDRAWN: Integrating Prior Knowledge and Data-Driven Approaches for Improving Grapheme-to-Phoneme Conversion in Korean Language

Cao,

Zhao,

2023

Preprint

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Currently, grapheme-to-phoneme (G2P) conversion technology is dominated by two methodologies: knowledge-based and data-based approaches. Knowledge-driven methods face challenges in adapting to extensive datasets, while data-driven methods heavily rely on high-quality data and require precise feature selection for model construction. To overcome these challenges, this research proposes an integrated approach that combines prior knowledge with data-driven techniques for automatic G2P conversion in the Korean language. We extract attributes based on pronunciation rules and phonetic transformations between Korean words to construct a decision tree. Subsequently, the model is trained using the data-driven approach for automated phonetic transcription. The proposed model achieves more accurate alignment between input and output variables, effectively capturing phonological variations in continuous Korean speech and determining corresponding phonemes for graphemes. Rigorous cross-validation confirms its superiority, with an average accuracy of 94.63% in grapheme-to-phoneme conversion, outperforming existing methodologies.

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Section: Knowledge-driven G2p Approachmentioning

confidence: 99%

WITHDRAWN: Integrating Prior Knowledge and Data-Driven Approaches for Improving Grapheme-to-Phoneme Conversion in Korean Language

Cao,

Zhao,

2023

Preprint

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“…G2P systems allow users to enforce the desired pronunciation by providing a phonetic transcript of the input. Instead of models, rule based G2P conversion is another alternative, provided linguistic specialists have created these conversion rules [18].…”

Section: Making Speech Sound Naturalmentioning

confidence: 99%

Text-To-Speech in Voice Assistants: Challenges and Mitigation Strategies

Vishnu Kadam

2023

J Eng App Sci Technol

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Voice Assistants (VAs) have grown rapidly from technological novelties to integral parts of our daily lives to perform tasks like streaming music or news, setting alarms or responding to questions. These virtual conversational agents rely on an intricate combination of technologies, and one of the pivotal components is Text-to-Speech (TTS) synthesis. In this paper, we delve into the technical intricacies of TTS in voice assistants, addressing challenges, solutions, and future directions. VAs like Alexa, Siri and Google Assistant have transformed human-computer interactions. The underpinning TTS technology is crucial for converting text-based information into spoken language, making the interaction more natural and accessible. The synthesis of human-like speech from textual data is a complex and interdisciplinary domain, encompassing fields such as speech signal processing, natural language processing, deep learning, and linguistics. This paper aims to contribute a detailed analysis of TTS in voice assistants, emphasizing not only the theoretical aspects but also the practical implementation and real-world implications. The paper will examine the challenges associated with TTS, considering its technical, linguistic, and user-centric dimensions. The paper will also present mitigation strategies for these challenges. In a world where voice-driven interactions are becoming commonplace, a deep understanding of TTS is vital. By delving into the depths of this technology, we can unlock its full potential and ensure that voice assistants continue to enrich our lives and technical domains

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“…Solutions for automatic g2p conversion in one language may not be the optimal solution applicable for a different language. There are problems with different levels of difficulty that should be solved for each language or language family separately [13]. Malayalam is a morphologically complex low resource language with very little transcribed audio datasets and no openly available pronunciation lexicons.…”

Section: Motivationmentioning

confidence: 99%

“…Word processing speed (WPS) is one indicator of a g2p algorithm's effectiveness [13]. The WPS for the applications, Unified Parser [4], Espeak 7 and the proposed tool Mlphon was estimated by measuring the time required to convert the 100k common words in Malayalam listed in Indic NLP corpus [19] as per (2).…”

Section: A Comparison Of Processing Speedmentioning

confidence: 99%

Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers

2022

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In this article we present the design and the development of a knowledge based computational linguistic tool, Mlphon [em.el.foːɳ] for Malayalam language. Mlphon computationally models linguistic rules using finite state transducers and performs multiple functions including grapheme to phoneme (g2p) and phoneme to grapheme (p2g) conversions, syllabification, phonetic feature analysis and script grammar check. This open source software tool, released under MIT license, is developed as a one-stop solution to handle different speech related text processing tasks for automatic speech recognition, text to speech synthesis and nonspeech natural language processing tasks including syllable subword based language modeling, phoneme diversity analysis and text sanity check. The tool is evaluated on a manually crafted gold standard lexicon. Mlphon performs orthographic syllabification with 99% accuracy with a syllable error rate of 0.62% on the gold standard lexicon. For grapheme to phoneme conversion task, overall phoneme recognition accuracy of 99% with a phoneme error rate of 0.55% is obtained on gold standard lexicon. Additionally an extrinsic evaluation of Mlphon is performed by employing the pronunciation lexicon created using Mlphon, in Malayalam automatic speech recognition (ASR) task. Performance analysis in terms of the computation time of lexicon creation process and the word error rate (WER) on ASR task are presented along with a comparison over other automated tools for lexicon creation. Pronunciation lexicons with more than 100k commonly used Malayalam words in phonemised and syllabified forms is created and they are published as open language resources along with this work. We also demonstrate the usage of Mlphon on different natural language processing applications -syllable subword ASR, assisted pronunciation learning, phoneme diversity analysis and text sanity check. Being a knowledge based solution with open source code, Mlphon can be adapted to other languages of similar script nature.

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A Rule-Based Grapheme-to-Phoneme Conversion System

Cited by 7 publications

References 43 publications

WITHDRAWN: Integrating Prior Knowledge and Data-Driven Approaches for Improving Grapheme-to-Phoneme Conversion in Korean Language

WITHDRAWN: Integrating Prior Knowledge and Data-Driven Approaches for Improving Grapheme-to-Phoneme Conversion in Korean Language

Text-To-Speech in Voice Assistants: Challenges and Mitigation Strategies

Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers

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