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Journal articles on the topic 'Text-to-Speech'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

H J, Petkar. "Brief Review on Text - to - Speech System." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 1923–26. https://doi.org/10.21275/sr211018142638.

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2

Jadhav, Vishal. "Speech to Text - Text to Speech Transcription for Indian Languages." International Journal of Science, Engineering and Technology 13, no. 2 (2025): 1–8. https://doi.org/10.61463/ijset.vol.13.issue2.337.

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3

Y M, Manish, Kavya K, and Vishesh S. "Android Application to Convert Speech to Text and Text to Speech." IJARCCE 8, no. 2 (2019): 49–52. http://dx.doi.org/10.17148/ijarcce.2019.8208.

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4

Rudrappa, Naveenkumar T., Mallamma V. Reddy, and M. Hanumanthappa. "KHiTE: Multilingual Speech Acquisition to Monolingual Text Translation." Indian Journal Of Science And Technology 16, no. 21 (2023): 1572–79. http://dx.doi.org/10.17485/ijst/v16i21.727.

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5

Furui, S., T. Kikuchi, Y. Shinnaka, and C. Hori. "Speech-to-Text and Speech-to-Speech Summarization of Spontaneous Speech." IEEE Transactions on Speech and Audio Processing 12, no. 4 (2004): 401–8. http://dx.doi.org/10.1109/tsa.2004.828699.

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6

Venkateswarlu, S. "Text to Speech Conversion." Indian Journal of Science and Technology 9, no. 1 (2016): 1–3. http://dx.doi.org/10.17485/ijst/2016/v9i38/102967.

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7

Hallahan, William I., and Anthony J. Vitale. "Software text-to-speech." International Journal of Speech Technology 1, no. 2 (1997): 121–34. http://dx.doi.org/10.1007/bf02277193.

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8

Sproat, Richard W., and Joseph P. Olive. "Text-to-Speech Synthesis." AT&T Technical Journal 74, no. 2 (1995): 35–44. http://dx.doi.org/10.1002/j.1538-7305.1995.tb00399.x.

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9

Ramacharla, Sai Teja. "Speech to Text Transcription." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4172–76. http://dx.doi.org/10.22214/ijraset.2024.60714.

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Abstract: This paper aims to enhance speech recognition and audio processing, converting spoken sentences into text and taking input from various input sources like microphones, audio, and video files. Notably, it offers robust audio conversion capabilities, supporting MP3 to WAV and other formats. To enhance the scalability and user experience, the system is implemented as a Flask-powered web application, providing users with a seamless interface accessible through a Flask-powered web browser serves to facilitate intuitive and user-friendly communication, making the application versatile, dif

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10

Kothadiya, Deep, Nitin Pise, and Mangesh Bedekar. "Different Methods Review for Speech to Text and Text to Speech Conversion." International Journal of Computer Applications 175, no. 20 (2020): 9–12. http://dx.doi.org/10.5120/ijca2020920727.

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11

Utkarsh, Verma, and Padmanaban R. Dr. "Speech Cloning: Text-To-Speech Using VITS." Engineering and Technology Journal 9, no. 05 (2024): 3951–56. https://doi.org/10.5281/zenodo.11158985.

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Voice is one of the most common and natural communication methods for humans. Voice is becoming the primary interface for AI voice assistants like Amazon Alexa, as well as in autos and smart home devices. Homes and so on. As human-machine communication becomes more common, researchers are exploring technology that mimics genuine speech. Speech cloning is the practice of copying or mimicking another person's speech, usually utilizing modern technology and artificial intelligence (AI). This entails producing a synthetic or cloned version of someone's voice that sounds very similar to the actual

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12

S., Priyadharshini. "Image Text to Speech Conversion Using Optical Character Recognition." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 4199–205. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020134.

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13

King, Simon. "Measuring a decade of progress in Text-to-Speech." Loquens 1, no. 1 (2014): e006. http://dx.doi.org/10.3989/loquens.2014.006.

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14

Nath, Chandamita, and Bhairab Sarma. "AI Enabled Text-to-Speech Synthesis for Unicode Language." Indian Journal Of Science And Technology 17, no. 42 (2024): 4454–61. http://dx.doi.org/10.17485/ijst/v17i42.2645.

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Objectives: To explore the advancements and challenges in the implementation of an AI-enabled Text-to-Speech (TTS) for the Assamese language (an Indo-Aryan language) by discussing technological approaches, linguistic considerations, and its potential applications. Methods: The developed system has been experimented with 40K (approx) collected words of five different categories and tested with different datasets for each model. Four prominent methods (Dictionary, HMM, CNN & G2P) are commonly found used in TTS. With the Grapheme- to- Phoneme (G2P) conversion technique, all phonemes are order

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15

Sunitha, Dr K. V. N., and P. Sunitha Devi. "Text Normalization for Telugu Text-to-Speech Synthesis." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 2 (2013): 2241–49. http://dx.doi.org/10.24297/ijct.v11i2.1176.

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Most areas related to language and speech technology, directly or indirectly, require handling of unrestricted text, and Text-to-speech systems directly need to work on real text. To build a natural sounding speech synthesis system, it is essential that the text processing component produce an appropriate sequence of phonemic units corresponding to an arbitrary input text. A novel approach is used, where the input text is tokenized, and classification is done based on token type. The token sense disambiguation is achieved by the semantic nature of the language and then the expansion rules are

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16

Arsalan, Ansari. "Lenspeak: Text to Speech Glasses." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41025.

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This research explores the creation of "Text-to- Speech (TTS) Glasses," an innovative assistive device designed to enhance accessibility for individuals with visual impairments. The glasses facilitate the real-time transformation of printed text into audible speech, empowering users to independently access textual information. Utilizing a Raspberry Pi as he processing core, the system integrates advanced Optical Character Recognition (OCR) and Text-to-Speech (TTS) technologies. This paper delves into the system’s architecture, functionality, benefits, limitations, and prospective advancements

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17

Olive, J. P., and M. Y. Liberman. "Text to speech—An overview." Journal of the Acoustical Society of America 78, S1 (1985): S6. http://dx.doi.org/10.1121/1.2022951.

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18

Colwell, Judy. "Speech-to-text communication access." Hearing Journal 55, no. 11 (2002): 79. http://dx.doi.org/10.1097/01.hj.0000324181.04726.44.

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19

Lin, Kun‐Shan. "Text‐to‐speech synthesis system." Journal of the Acoustical Society of America 86, no. 5 (1989): 2051–52. http://dx.doi.org/10.1121/1.398486.

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20

Kamanaka, Hiroki. "Text-to-speech conversion system." Journal of the Acoustical Society of America 125, no. 6 (2009): 4108. http://dx.doi.org/10.1121/1.3155496.

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21

O'Malley, M. H. "Text-to-speech conversion technology." Computer 23, no. 8 (1990): 17–23. http://dx.doi.org/10.1109/2.56867.

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22

Socher, Godrun. "Intelligent text-to-speech synthesis." Journal of the Acoustical Society of America 114, no. 1 (2003): 31. http://dx.doi.org/10.1121/1.1601090.

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23

Didcock, Cliff. "Shared text-to-speech resource." Journal of the Acoustical Society of America 114, no. 5 (2003): 2546. http://dx.doi.org/10.1121/1.1634119.

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24

Rampey, Fred D., and James M. MacMillan. "Speech to text conversion system." Journal of the Acoustical Society of America 121, no. 5 (2007): 2493. http://dx.doi.org/10.1121/1.2739197.

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25

Bothe, Prathamesh. "Image-to-Text-Speech Converter." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04174.

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ABSTRACT This paper presents the development of an Image-to-Text-Speech Converter, a Python-based assistive system designed to extract and vocalize text from images. The primary goal of the project is to support visually impaired individuals and those with reading difficulties by converting printed or digital text into clear, audible speech. The system integrates Optical Character Recognition (OCR) using Tesseract with Text-to-Speech (TTS) synthesis using tools like pyttsx3 or gTTS. Preprocessing techniques such as grayscale conversion, noise reduction, and thresholding are applied through Ope

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26

SPROAT, RICHARD. "Multilingual text analysis for text-to-speech synthesis." Natural Language Engineering 2, no. 4 (1996): 369–80. http://dx.doi.org/10.1017/s1351324997001654.

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We present a model of text analysis for text-to-speech (TTS) synthesis based on (weighted) finite state transducers, which serves as the text analysis module of the multilingual Bell Labs TTS system. The transducers are constructed using a lexical toolkit that allows declarative descriptions of lexicons, morphological rules, numeral-expansion rules, and phonological rules, inter alia. To date, the model has been applied to eight languages: Spanish, Italian, Romanian, French, German, Russian, Mandarin and Japanese.

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27

Abhale, Prof Priyanka. "Text Summarization and Conversion of Speech to Text." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6322–24. http://dx.doi.org/10.22214/ijraset.2023.52902.

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Abstract: This article describes the fusion of recurrent neural networks and deep learning algorithms for text summarization systems and analysis of the text learning process. Next, the text analytics learning model is summarized. In addition, applications of deep learning-based text analysis are also introduced. Language is the most important part of communication between people. Although there are many ways to express our thoughts and feelings, language is considered the most important medium of communication. Speech recognition is the process by which machines recognize different people's v

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28

Ravikiran, Pichika, and Midhun Chakkaravarthy. "Enhancing Speech-to-Text Conversion with Convolutional Reinforcement Learning Algorithms." International Journal of Science and Research (IJSR) 13, no. 8 (2024): 1118–22. http://dx.doi.org/10.21275/sr24515225027.

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29

Rani, Kanchan, Prachi Agarwal, Rachit Tyagi, Aanchal Choudhary, Pranjal Agarwal, and Shadan Shadan. "Sign Language to Text and Speech Conversion Using Machine Learning." International Journal of Research Publication and Reviews 6, sp5 (2025): 147–56. https://doi.org/10.55248/gengpi.6.sp525.1920.

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30

Valentini-Botinhao, Cassia, and Junichi Yamagishi. "Speech Enhancement of Noisy and Reverberant Speech for Text-to-Speech." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 8 (2018): 1420–33. http://dx.doi.org/10.1109/taslp.2018.2828980.

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31

Kuzmin, A., and S. Ivanov. "Speech to Text System for Noisy and Quiet Speech." Journal of Physics: Conference Series 2096, no. 1 (2021): 012071. http://dx.doi.org/10.1088/1742-6596/2096/1/012071.

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Abstract This paper examines one of the available and simple methods to develop speech recognition systems capable of recognizing speech from noisy or silent recordings. Such systems improve the automated operation of call centers, and also bring us closer to creating speech recognition models capable of ignoring the speech deficiencies of speakers.

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32

Supriya, Dhanaraj Dhumale, Vitthal Khopade Manjiri, Dhimate Bhushan, and Yogesh Dhere Avadhoot. "A Brief Survey on Emotion Based Text to Speech Conversion System." International Journal of Soft Computing and Engineering (IJSCE) 11, no. 1 (2021): 40–43. https://doi.org/10.35940/ijsce.A3529.0911121.

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Text to speech conversion is one of the applications of machine learning. It is widely used in search engines, standalone applications, web applications, chatbots and android applications. But still there is need to upgrade text to speech system so that we can get more interactive and user-friendly application. Traditional text to speech application has monotonous voice as output which does not has emotions in it and seems to be more mechanized. So, there is need to improvise the existing system by embedding the flavour of emotions in it. Existing text to speech cannot be used in story telling

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33

Kumar, Dr M. Upendra. "Speech to multi-language text conversion." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 6 (2021): 349–63. http://dx.doi.org/10.54660/.ijmrge.2021.2.6.349-363.

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Right from the beginning of previous century, researchers have shown interest in areas like Automatic Speech Recognition, Image Processing and Natural Language Processing. The area of Automatic Speech Recognition (ASR) has received attention over the past five decades due to its application in both commercial and military. In the recent times this can be attributed to the advancements in Artificial Intelligence and Advanced Algorithms. ASR takes speech as input and converts it in to text. ASR is employed in electronic dictionaries, Customer Call Centers, Voice Dictation and Query based Informa

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34

C, Vinola, and Radhika K. "LIP TO SPEECH AND TEXT SYNTHESIS." International Research Journal of Computer Science 09, no. 09 (2022): 372–75. http://dx.doi.org/10.26562/irjcs.2022.v0909.05.

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Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise.In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. Acknowledging the importance of contextual and speaker specific cues for accurate lip-reading, we take a different path from existing works.We focus on learning accurate lip sequences to speech mappings for individual speakers in unconstrained, large vocabulary settings. We collect and release a large-scale benchma

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35

GAUVAIN, J. L., L. F. LAMEL, G. ADDA, and J. MARIANI. "SPEECH-TO-TEXT CONVERSION IN FRENCH." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (1994): 99–131. http://dx.doi.org/10.1142/s021800149400005x.

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Speech-to-text conversion of French necessitates that both the acoustic level recognition and language modeling be tailored to the French language. Work in this area was initiated at LIMSI over 10 years ago. In this paper a summary of the ongoing research in this direction is presented. Included are studies on distributional properties of French text materials; problems specific to speech-to-text conversion particular of French; studies in phoneme-to-grapheme conversion for continuous, error-free phonemic strings; past work on isolated-word speech-to-text conversion; and more recent work on co

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36

Stadler, Kurt J. "Text-to-speech conversion for German text with TALKMAN." Journal of Microcomputer Applications 11, no. 4 (1988): 317–36. http://dx.doi.org/10.1016/0745-7138(88)90009-7.

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37

Choi, Yeunju, Youngmoon Jung, Younggwan Kim, Youngjoo Suh, and Hoirin Kim. "An end-to-end synthesis method for Korean text-to-speech systems." Phonetics and Speech Sciences 10, no. 1 (2018): 39–48. http://dx.doi.org/10.13064/ksss.2018.10.1.039.

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38

Poojary, Nigam R., and K. H. Ashish. "Text To Speech with Custom Voice." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4523–30. http://dx.doi.org/10.22214/ijraset.2023.51217.

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Abstract: The Text to Speech with Custom Voice system described in this work has vast applicability in numerous industries, including entertainment, education, and accessibility. The proposed text-to-speech (TTS) system is capable of generating speech audio in custom voices, even those not included in the training data. The system comprises a speaker encoder, a synthesizer, and a WaveRNN vocoder. Multiple speakers from a dataset of clean speech without transcripts are used to train the speaker encoder for a speaker verification process. The reference speech of the target speaker is used to cre

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39

Duc, Chung Tran, Long Nguyen Duc, and Fadzil Hassan Mohd. "Development and testing of an FPT.AI-based voicebot." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2388–95. https://doi.org/10.11591/eei.v9i6.2620.

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In recent years, voicebot has become a popular communication tool between humans and machines. In this paper, we will introduce our voicebot integrating text-to-speech (TTS) and speech-to-text (STT) modules provided by FPT.AI. This voicebot can be considered as a critical improvement of a typical chatbot because it can respond to human’s queries by both text and speech. FPT Open Speech, LibriSpeech datasets, and music files were used to test the accuracy and performance of the STT module. For the TTS module, it was tested by using text on news pages in both Vietnamese and English. To tes

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40

V, Dr Rukkumani, Dr Radhika V, Devasena D, and Dr Sharmila B. "Lip Reading and Speech to Text Converter for Deaf and Mute." International Journal of Research in Arts and Science 5, Special Issue (2019): 57–64. http://dx.doi.org/10.9756/bp2019.1001/07.

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41

Evans, Nicola. "Speak It! Text to Speech app." Nursing Standard 28, no. 48 (2014): 31. http://dx.doi.org/10.7748/ns.28.48.31.s38.

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42

Wankhade, Gauri. "Resonate: Website on Text to Speech." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 1479–94. http://dx.doi.org/10.22214/ijraset.2022.44060.

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Abstract: Synthesizing speech is quite complex as it is heavily reliant on language. Meaning, the language processing section in a TTS system inherently has the largest chunk of linguistic knowledge for a particular language. The technical as well as theoretical challenges faced while building such a high-quality system can be quite daunting and hard to navigate. To ensure that the system has relevant and updated linguistic information, one must make sure it has access to the most natural and unrestricted text to ensure quality and authenticity. We will also need extensive studies to achieve t

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43

Kennedy, Gerry. "Benefits of text to speech software." Australian Journal of Learning Disabilities 8, no. 3 (2003): 31–34. http://dx.doi.org/10.1080/19404150309546737.

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44

Karn, Helen E. "The Hablarte text‐to‐speech system." Journal of the Acoustical Society of America 103, no. 5 (1998): 2777. http://dx.doi.org/10.1121/1.422243.

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45

Hon, Hsiao-Wuen. "Proofreading with text to speech feedback." Journal of the Acoustical Society of America 115, no. 1 (2004): 22. http://dx.doi.org/10.1121/1.1647015.

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46

Eide, Ellen M. "Training of text-to-speech systems." Journal of the Acoustical Society of America 115, no. 5 (2004): 1874. http://dx.doi.org/10.1121/1.1757180.

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47

Christfort, Jacob. "Adaptive hosted text to speech processing." Journal of the Acoustical Society of America 120, no. 2 (2006): 580. http://dx.doi.org/10.1121/1.2336693.

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48

Lindquist, Benjamin. "The Art of Text-to-Speech." Critical Inquiry 50, no. 2 (2024): 225–51. http://dx.doi.org/10.1086/727651.

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49

Journal, IJSREM. "A Review on Speech-to-Text." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem29004.

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The current era represents the global apex of groundbreaking advances in artificial intelligence (AI) technology, especially in the field of speech-to-text (STT).This review article focuses on the development of human skills through smooth, natural language interaction between people and robots, providing a thorough overview and exploration of the impressive advancements made in recent years. The study outlines a model intended to reinvent human-computer interaction, highlighting its ability to translate spoken language into text and carry out commands via a conversational, dynamic interface.

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50

Nishu, Kumari. "Image to Text and Speech Conversion." International Journal of Innovative Science and Research Technology 7, no. 12 (2023): 1965–67. https://doi.org/10.5281/zenodo.7551296.

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This text-to-image convertor aims to check the conversion of data between the various modalities (text, image) because of the evolution of human-machine communication that introduced the utilization of natural communication modalities to humans. Such as gestures, speech, sound, and vision. In fact, one of the main challenges of this "multimodal" learning is the learning of a shared illustration between the distinct modalities and the prediction of the missing knowledge ( by retrieval or synthesis) from one conditioned modality to another. Some researchers work on the various varietie

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