Relevant bibliographies by topics / Google Translate's text-to-speech API

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Academic literature on the topic 'Google Translate's text-to-speech API'

Author: Grafiati
Published: 5 June 2025
Last updated: 25 June 2025

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Journal articles on the topic "Google Translate's text-to-speech API"

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Varuzhan, Harutyun Baghdasaryan. "Clean and Noisy Datasets Generation for DeepSpeech Open-Source Speech-To-Text Engine Based on Google Translate API." Journal of Scientific and Engineering Research 8, no. 2 (2021): 23–25. https://doi.org/10.5281/zenodo.10574882.

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<strong>Abstract</strong> Speech-to-text engines use both clean and noisy datasets to train models for best performance. But for some languages (for example, Armenian language) there is no enough data for training. The purpose of this article is to design a tool that can generate both clean and noisy(additive white Gaussian noise(AWGN) and real-world noise(RWN)) datasets for DeepSpeech speech-to-text engine using Google Translate's text-to-speech API feature that can convert text to normal and slow speech.
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Wydyanto. "Converting Image Text to Speech Using Raspberry Pi." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 651–58. https://doi.org/10.52783/jisem.v10i34s.5860.

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This project aims to develop a system that can convert image text into speech using Raspberry Pi. This system will use optical character recognition (OCR) technology to extract text from images, which will then be converted into speech using text-to-speech software. (TTS). (TTS). This will provide a valuable tool for individuals with visual impairments or those who have difficulty reading text in images. This research explores a Raspberry Pi-based device that can translate English into 53 dialects, using a camera module, OCR motor, Google Speech API, and Microsoft Translator. This feature is accessible to visually impaired individuals and those who do not speak English. Raspberry Pi, with the Tesseract OCR engine, Google Voice API, Microsoft Translator, and camera board, enables real-time text translation on images, making it more accessible and inclusive for users with visual impairments or language barriers.
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Singh, Mr Akarsh. "AI Tool/Mobile App for Indian Sign Language (ISL)." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2819–24. https://doi.org/10.22214/ijraset.2025.71056.

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Abstract: This project proposes an AI-powered Sign Language Generator for Audio-Visual Content in English/Hindi that leverages cutting-edge technologies to bridge this communication gap. The system captures spoken language using advanced speech recognition techniques provided by the Google Speech Recognition API, transcribing speech into text with high accuracy. When inputs are in Hindi, the system employs the Google Translate API to convert the text into English, ensuring a standardized vocabulary that maps to ISL gestures.
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Nenny, Anggraini, Kurniawan Angga, Kesuma Wardhani Luh, and Hakiem Nashrul. "Speech Recognition Application for the Speech Impaired using the Android-based Google Cloud Speech API." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 6 (2018): 2733–39. https://doi.org/10.12928/TELKOMNIKA.v16i6.9638.

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Those who are speech impaired (tunawicara in the Indonesian language) suffer from abnormalities in their delivery (articulation) of the language as well their voice in normal speech, resulting in difficulty in communicating verbally within their environment. Therefore, an application is required that can help and facilitate conversations for communication. In this research, the authors have developed a speech recognition application that can recognise speech of the speech impaired, and can translate into text form with input in the form of sound detected on a smartphone. By using the Google Cloud Speech Application Programming Interface (API), this allows converting audio to text, and it is also user-friendly to use such APIs. The Google Cloud Speech API integrates with Google Cloud Storage for data storage. Although research into speech recognition to text has been widely practiced, this research try to develop speech recognition, specially for speech impaired&#39;s speech, as well as perform a likelihood calculation to see the factor of tone, pronunciation, and speech speed in speech recognition. The test was conducted by mentioning the digits 1 through 10. The experimental results showed that the recognition rate for the speech impaired is about 80%, while the recognition rate for normal speech is 100%.
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Kale, Shubham, Basavaraj Kumbhar, Hitesh Patil, Karan Jaitpal, and Dr Swati Sinha. "IoT based Smart Book Reader for Visually Impaired." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4535–39. http://dx.doi.org/10.22214/ijraset.2023.51236.

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Abstract: This paper presents an IoT- based smart book reader for visually impaired individuals, using a Raspberry Pi computer and various software tools. The system allows users to capture images of printed material using a camera, which are then processed using Tesseract OCR software to extract text. The extracted text is then translated from English to Marathi using the Google Cloud Translation API, and converted to speech using the Google Text-to-Speech API. The system is designed to be operated using a single hardware button, making it easy and intuitive for users with visual impairments. The proposed system offers a low-cost and portable solution for visually impaired individuals to access printed material, and has the potential to improve their quality of life.
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N, KEERTHI, CHETANA P. SUTHAR, and LEKHANA E. "REAL-TIME ACCENT TRANSLATOR." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40714.

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This paper introduces the "Real-Time Accent Translator," a lightweight and accessible web-based application that bridges the gap between multilingual communication and accent adaptation. Built on Flask, the system integrates Google Translate and Google Text-to-Speech (gTTS) APIs to provide seamless translation and speech synthesis. Users can input text in a source language, specify the target language, and optionally adjust accents for languages such as English. The application translates the text, synthesizes speech with the desired accent, and provides an audio output in real time. The architecture is designed for simplicity, leveraging third-party APIs to ensure rapid deployment and scalability without the need for extensive computational resources. This paper discusses the technical implementation, including API integration, RESTful communication, and real-time audio generation. Additionally, the potential use cases of the system are highlighted, including cross-cultural communication, language learning, and accessibility for non-native speakers.
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B, SAMBAVI. "Developing A Software That Can Translate Resource Material and Other Texts from English to Other Indian Regional Language." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47848.

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ABSTRACT—The linguistic diversity of India is both a cultural asset and a major communication challenge. With 22 official languages and hundreds of dialects, the need for efficient translation tools is critical. While global solutions like Google Translate provide multilingual translation capabilities, there is still a shortage of localized, accessible, and user-friendly applications specifically for Indian languages.This work presents the Indian Language Translator, a desktop application based on Python that translates text in real-time between English and prominent Indian languages through a simple graphical user interface (GUI) developed using Tkinter and the Google Translate API. The project's motivation, system design, implementation approaches, evaluation, and its potential impacts are discussed in the research. Future work directions for offline translation, speech integration, and further language support are also explored. Keywords—Indian Languages, Language Translation, GUI Development, Machine Translation, Tkinter, Google Translate API, Python Applications, Natural Language Processing (NLP)
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S. Nikkam, Pushpalatha. "Voice To Sign Language Conversion." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48637.

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ABSTRACT True incapacity can be seen as the inability to speak, where individuals with speech impairments struggle to communicate verbally or through hearing. To bridge this gap, many rely on sign language, a visual method of communication that uses hand gestures. Although sign language has become more widespread, interaction between those who sign and those who don't can still pose challenges. As communication has grown to be an essential part of daily life, sign language serves as a crucial tool for those with speech and hearing difficulties. Recent advances in computer vision and deep learning have significantly enhanced the ability to recognize gestures and movements. While American Sign Language (ASL) has been thoroughly researched, Indian Sign Language (ISL) remains underexplored. Our proposed approach focuses on recognizing 4972 static hand gestures representing 24 English alphabets (excluding J and Z) in ISL. The project aims to build a deep learning-based system that translates these gestures into text using the "Google Text-to-Speech" API, thereby enabling better interaction between signers and non-signers. Using a dataset from Kaggle and a custom Convolutional Neural Network (CNN), our method achieved a 99% accuracy rate. Key Words: Convolutional Neural Network; Google text to speech API; Indian signing.
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Gopal D. Upadhye. "Multilingual Language Translator Using ML." Advances in Nonlinear Variational Inequalities 28, no. 2s (2024): 37–46. https://doi.org/10.52783/anvi.v28.2512.

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This research paper explores the design and development of a Python-based multilingual translation application that leverages various libraries for a robust, user-friendly experience. The application integrates multiple functions such as text translation, speech-to-text, text-to-speech, and PDF text extraction, using libraries like Pyttsx3, PyPDF2, Speech Recognition, Tkinter, and the Google Translate API. The system allows for real-time translations, enhancing communication across different languages and improving accessibility. It enables users to convert PDF content into translated text and provides voice-based input for ease of use, particularly for users with physical limitations. The application’s performance in translation accuracy, speech recognition, and ease of use has been thoroughly tested, yielding positive user feedback. Furthermore, the modular design of the system allows for easy scalability and adaptability for future improvements, such as integrating more languages and enhancing voice recognition. This project demonstrates the effective use of Python’s rich library ecosystem in creating a comprehensive tool to bridge language barriers in various personal, academic, and professional contexts.
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Shabrina, Marwati Maryam, Arik Aranta, and Budi Irmawati. "RANCANG BANGUN ALGORITMA KONVERSI SUARA BERBAHASA INDONESIA MENJADI TEKS LATIN BERBAHASA SASAK MENGGUNAKAN METODE DICTIONARY BASED." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 6, no. 1 (2024): 364–75. http://dx.doi.org/10.29303/jtika.v6i1.371.

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As time goes by, the use of the Sasak language among the people of Lombok is decreasing. In fact, the Sasak language is the identity of the island of Lombok which needs to be preserved as a heritage for the younger generation. The increasingly rapid development of technology has encouraged the emergence of innovation in creating various inventions that can facilitate human activities. One innovation that can be developed is speech to text technology. This technology can recognize human voices and then convert them into text. This is of interest to the author in designing a system that implements Google’s speech to text API to translate Indonesian words or sentences into Sasak. The translation from Indonesian to Sasak was carried out by applying a dictionary based system to produce an appropriate translation. The testing process was carried out by translating 25 sentences taken from the Sasak-Indonesian Dictionary and consisting of 117 words. In this research, there were two stages of testing carried out. The first test was carried out to determine the accuracy of the results of the Indonesian translation into Sasak using the dictionary based method. The second test was carried out to determine the accuracy of the Google Speech API in recognizing voice input and then converting it into text. From the first test, the system accuracy results in translating Indonesian to Sasak using the dictionary based method were 100% and the error rate was 0%. Meanwhile, from the second test, the results showed that the system could implement the Google Speech API to translate Indonesian words or sentences into Sasak with an accuracy of 99.14%.
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Dissertations / Theses on the topic "Google Translate's text-to-speech API"

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Rajashekar, Raksha. "Speech Enabled Navigation in Virtual Environments." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567554934550569.

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Book chapters on the topic "Google Translate's text-to-speech API"

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More, Priyanka, Sachin Sakhare, Rahul Shelke, Saurabh Raut, Yugandhar Patil, and Darshan Vora. "Enhancing Independence Computer Vision-Based Object Detection Techniques for the Visually Impaired." In Modern Digital Approaches to Care Technologies for Individuals With Disabilities. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7560-0.ch014.

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This paper introduces an innovative solution leveraging the advanced YOLOv8 deep learning model to provide real-time object detection for over 2 billion blind and visually impaired individuals. Acting as a virtual “eye,” the system allows users to recognize objects and environments with high precision and speed. By integrating the Google Text-to-Speech API, it delivers intuitive voice guidance, offering immediate audio feedback for identified objects. This approach addresses the key challenges of accessibility and independence faced by the visually impaired and opens doors for more advanced assistive technologies. The system combines YOLOv8's robustness, the versatility of Raspberry Pi, and the efficiency of text-to-speech to create a comprehensive object detection solution, designed to enhance users' navigation and boost their confidence in daily activities. This research represents a major step forward in assistive technology, offering practical solutions that significantly improve the quality of life for those with visual impairments.
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Godwin-Jones, Robert, Errol O'Neill, and Jim Ranalli. "Integrating AI Tools into Instructed Second Language Acquisition." In Exploring AI in Applied Linguistics. Iowa State University Digital Press, 2024. http://dx.doi.org/10.31274/isudp.2024.154.02.

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The public availability of ChatGPT and other AI tools is having a transformative effect on our lives in a variety of domains including education. Many language learners have for some time been avid users of AI-enabled tools such as Google Translate, text editors like Grammarly, or voice assistants. While some uses of AI products in L2 teaching and learning have generally been seen positively, the use of others has been controversial. This chapter addresses issues surrounding the integration of AI tools into instructed SLA (second language acquisition), focusing on machine translation, chatbots, and AI-based tools for written corrective feedback. It suggests that, based on existing research studies and reports on teaching practices using AI tools, L2 instructors should adopt a critical, balanced approach to AI integration in L2 instruction that leverages generative AI’s strengths while taking into account its downsides.
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Conference papers on the topic "Google Translate's text-to-speech API"

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Varada, Sai Vamsi, and Sagar Pande. "Extracting and translating a large video using Google cloud speech to text and translate API without uploading at Google cloud." In THE FOURTH SCIENTIFIC CONFERENCE FOR ELECTRICAL ENGINEERING TECHNIQUES RESEARCH (EETR2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0162802.

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Choi, Jungyoon, Haeyoung Gill, Soobin Ou, Yoojeong Song, and Jongwoo Lee. "Design of Voice to Text Conversion and Management Program Based on Google Cloud Speech API." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00286.

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Sjöbergh, Jonas, and Viggo Kann. "Granska API – an Online API for Grammar Checking and Other NLP Services." In Eighth Swedish Language Technology Conference (SLTC-2020), 25-27 November 2020. Linköping University Electronic Press, 2021. http://dx.doi.org/10.3384/ecp184175.

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We present an online API to access a number of Natural Language Processing services developed at KTH. The services work on Swedish text. They include tokenization, part-of-speech tagging, shallow parsing, compound word analysis, word inflection, lemmatization, spelling error detection and correction, grammar checking, and more. The services can be accessed in several ways, including a RESTful interface, direct socket communication, and premade Web forms. The services are open to anyone. The source code is also freely available making it possible to set up another server or run the tools locally. We have also evaluated the performance of several of the services and compared them to other available systems. Both the precision and the recall for the Granska grammar checker are higher than for both Microsoft Word and Google Docs. The evaluation also shows that the recall is greatly improved when combining all the grammar checking services in the API, compared to any one method, and combining services is made easy by the API.
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