Text to speech synthesizer-formant synthesis

Lukose, Sneha; Upadhya, Savitha S.

doi:10.1109/icnte.2017.7947945

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…• Unit selection synthesis: Unit selection synthesis utilizes large databases of recorded speech [50]. This technique delivers the highest level of naturalness to the output, which is comparable to human voice.…”

Section: Concatenative Synthesismentioning

confidence: 99%

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Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Chemnad

Othman

2023

IEEE Access

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Although there are several speech synthesis models available for different languages tailored to specific domain requirements and applications, there is currently no readily available information on the latest trends in Arabic language speech synthesis. This can make it challenging for beginners to research and develop text-to-speech (TTS) systems for Arabic languages. To address this issue, this article provides a comprehensive overview of several scholars' contributions to the field of Arabic TTS, along with an examination of the unique features of the Arabic language and the corresponding challenges in creating TTS systems. Reporting only on papers discussing Arabic TTS, this systematic review evaluated the available literature published between 2000 and 2022. We conducted a systematic review in six databases using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to identify studies that addressed Arabic Text-to-Speech systems. Of a total of 3719 articles identified, only 36 (0.96%) articles met our search criteria. Bibliometric analyses of these studies were conducted and reported. The results highlight the main types of speech synthesis techniques used in TTS systems: concatenative, formant, deep neural network (DNN), hybrid models, and multiagent. The corpora used to develop these systems, as well as the diacritization techniques incorporated, evaluation techniques, and the results of the performance of the systems are reported. Subjective evaluation using the mean opinion score is most applied to measure the accuracy of systems. This study also identifies gaps in the literature and makes recommendations for future research directions.

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Section: Concatenative Synthesismentioning

confidence: 99%

“…This technique delivers the highest level of naturalness to the output, which is comparable to human voice. • Diphone synthesis: Compared with unit selection, diphone synthesis employs a smaller database [50]. • Domain-specific synthesis: Domain-specific synthesis is employed when the output vocabulary is limited [50].…”

Section: Concatenative Synthesismentioning

confidence: 99%

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Chemnad

Othman

2023

IEEE Access

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“…Formant synthesizers use acoustic models based on source-filter theory (9,10) and the main components used to model human speech production system is shown in Figure 1 . The idea behind source-filter model of human speech production mechanism can be expressed as Equation 1 in frequency domain.…”

Section: Articulatory Synthesismentioning

confidence: 99%

Comparative Analysis of Kannada Formant Synthesized Utterances and their Quality

D’Souza¹,

Ravi²

2023

IJST

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Objectives:The goal of this work is to synthesize Kannada utterances using a modified Klatt type formant synthesizer to evaluate its performance by comparing against eSpeak synthesizer in terms of intelligibility and quality of the utterances generated. Methods: Kannada utterances viz., vowels, diphthongs, Consonant-Vowel (CV) coarticulations and simple words are generated using a modified Klatt type formant synthesizer and eSpeak. The vowels and diphthongs generated by both the synthesizers are compared with natural recorded utterances using F1-F2 formants and the CV co-articulations are compared using spectrograms. The synthesized word utterances are compared with natural recorded utterances using Log Spectral Distance to find out which synthesizer outputs the frequency spectrum that is closest to the frequency spectrum of the natural utterances. Also, the synthesized word utterances are evaluated for their intelligibility and quality using Mean Opinion Score (MOS) obtained from 10 native Kannada language speakers. Findings: The word utterances synthesized by the modified Klatt type formant synthesizer scored a MOS of 86% and 4.46 out of 5 for the parameters of intelligibility and quality whereas for the same two parameters eSpeak scored 70% and 4.14 out of 5 respectively. Novelty: Klatt type formant synthesizer that uses pitch synchronous parameter update method synthesizes good quality Kannada sound utterances and storing the control parameters of the synthesizer using polynomials reduces the database footprint.

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“…It helps to model the human speech making system like vocal tract system and articulatory procedures straightly. If someone having deficiency of knowledge of the complex human articulation organs for them it is also very difficult to implement such method [8].…”

Section: B Articulation Speech Synthesismentioning

confidence: 99%

Text to Speech Synthesis System for Punjabi language using Statistical Parametric Speech Synthesis Technique

2019

IJITEE

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Statistical Parametric Speech Synthesis has been most growing technique rather than the traditional approaches that we are used to synthesizing the speech. The shortcoming of traditional approaches will be overcome with latest statistical techniques. The main advantages of SPSS from traditional synthesis technique are that it has more flexibility to change the characteristics of voice and support more multiple languages i.e. multilingual, has good coverage of acoustic ` and robustness. It generates high quality of speech from small training database. Deep Neural network and Hidden Morkov model are basic statistical parametric speech synthesis techniques. Gaussian mixture model, sinusoidal model are also under this categories. Features were extracted in two type spectral features like spectral bandwidth, spectral centroid etc. and excitation features like F0 frequencies etc. We are using 722 Punjabi phonemes. Using sound forge software we extracted the 200 wave file from 1 hour pre-recording wave file related to those phonemes. Each and every phonemes feature was extracted and saved in database. We were extracting 28 features of each phoneme. TTS text-to-speech system generates sounds or speech as a output when provided the text of Punjabi language. There were already many TTS are developed on different Indian languages. The system that we are trying to build is based only on Punjabi language.

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Text to speech synthesizer-formant synthesis

Cited by 14 publications

References 4 publications

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Comparative Analysis of Kannada Formant Synthesized Utterances and their Quality

Text to Speech Synthesis System for Punjabi language using Statistical Parametric Speech Synthesis Technique

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