Gaussian Filter-Based Speech Segmentation Algorithm for Gujarati Language

Gujarathi, Priyanka Vishwas; Patil, Sandip Raosaheb

doi:10.1007/978-981-16-1502-3_74

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Figure 1 shows color codes used to identify modules developed from scratch or modified/unmodified versions of existing standard modules. This system aims at translating a Tamil sentence into Tamil Speech and then voicing it out with emotions ensuring that there is no loss in the meaning conveyed and that the emotions are elegantly heard in the output (13) . The Morphological analyzer and POS Tagger rules are taken from Tamil grammar rules.…”

Section: Software Requirementsmentioning

confidence: 99%

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Arulprakash¹,

Synthiya²,

Vijila³

et al. 2023

IJST

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Objectives: Designing dynamic computer systems that are effective, efficient, simple, and satisfying to use is becoming extremely important in this age of information and communication technology. Text to Speech or Speech Synthesis is one of the many methods being investigated by researchers to improve Human-Computer Interaction. The goal here is to improve the text processing component of the Tamil voice synthesizer by including a text normalizer and loan word identification that is efficient and reliable. Methods: Text normalization is conducted on unconstrained Tamil text to turn non-standard terms into common words to reduce confusing utterances during intermediate word processing. Loan/Native words in Tamil literature are detected to enhance the Tamil voice synthesizer system's pronunciation model. Findings: During normalization, non-standard Tamil words are replaced with standard ones to reduce ambiguous utterances during interim processing. A pronunciation model is built to improve the Tamil speech synthesizer system by identifying loan words in Tamil text. A syllable classifier is presented in this study, based on a decision list approach, which can handle various types of non-stationary sounds. Novelty: We also disclose a 'loan/native word classifier' based on multiple linear regressions that perform well even with small words of three syllables. Such sophisticated text processors are required in today's dominating Digital, Information-Communication Technology, and Human-Computer Interaction age.

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Section: Software Requirementsmentioning

confidence: 99%

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Arulprakash¹,

Synthiya²,

Vijila³

et al. 2023

IJST

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“…Synthesized speech can be produced by employing several different techniques to find natural human-like sounds (7) . The speech synthesizer depends on the TTS synthesizer architecture inculcated to produce intelligible and natural sounds from the synthesizer (8) . Text-to-phoneme (T2P) is beleaguered to harvest phonetic transcription of the text, together with the wanted prosodic features (9) .…”

Section: Related Workmentioning

confidence: 99%

“…Research in the area of speech synthesis has been worked by the growing importance of many new applications which includes information retrieval services over the telephone such as banking services, public announcements at places like train stations, and reading out manuscripts for gathering (7) . Special tackle for the physically defied, such as word workstations with reading-out capability and book-reading aids for visually challenged and speaking aids for the vocally defied also use speech synthesis (8) . The perceived quality of standard general-purpose TTS systems is not good enough, which forces application developers to use pre-recorded prompts, drastically reducing the text generation plasticity.…”

Section: Introductionmentioning

confidence: 99%

Text to Speech Synthesizer for Tigrigna Linguistic using Concatenative Based approach with LSTM model

Araya¹,

Alehegn²

2022

IJST

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Objectives:The purpose of this study is to describe text-to-speech system for the Tigrigna language, using dialog fusion architecture and developing a prototype text-to-speech synthesizer for Tigrigna Language. Methods : The direct observation and review of articles are applied in this research paper to identify the whole strings which are represented the language. Tools used in this work are Mathlab, LPC, and python. In this paper LSTM deep learning model was applied to find out accuracy, precision, recall, and Fscore. Findings: The overall performance of the system in the word level which is evaluated by NeoSpeech tool is found to be 78% which is fruitful. When it comes to the intelligibility and naturalness of the synthesized speech in the sentence level, it is measured in MOS scale and the overall intelligibility and naturalness of the system are found to be 3.28 and 3.27 respectively. Based on the experiment LSTM Deep learning model provides an accuracy of 91.05%, the precision of 78.05%, recall of 86.59 %, and F-score of 83.05% respectively. The values of performance, intelligibility, and naturalness are inspiring and show that diphone speech units are good candidates to develop a fully functional speech synthesizer. Novelty: The researchers come up with the first text to speech LSTM deep learning model for the Tigrigna language which is critical and will be a baseline for other related research to be done for Tigrigna and other languages.

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Gaussian Filter-Based Speech Segmentation Algorithm for Gujarati Language

Cited by 2 publications

References 14 publications

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Text to Speech Synthesizer for Tigrigna Linguistic using Concatenative Based approach with LSTM model

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