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What is the state of the art for speech to text?

Speech synthesis

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State-of-the-art speech-to-text systems employ neural network-based approaches, such as Tacotron-2, to convert text into human-like speech output. These methods require a large amount of speech data, preferably recorded from a single speaker in a quiet environment, and have been successful in producing intelligible and natural synthetic speech. However, they face limitations in terms of data availability for low-resource languages. To address this, recent research has explored strategies for handling low-resource languages and developing multilingual text-to-speech systems. The aim is to provide insight into developing text-to-speech systems for Indian languages, given that India is a multilingual country. One approach is to make use of data from multiple languages to handle data scarcity.

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Proceedings Article•DOI Exploring Solutions for Text-to-Speech Synthesis of Low-Resource Languages Anushiya Rachel Gladston, K. V. Pradeep - Show less +1 more 23 Mar 2023	The provided paper is about exploring solutions for text-to-speech synthesis of low-resource languages. It does not discuss the state of the art for speech-to-text.
Proceedings Article•DOI Exploring Solutions for Text-to-Speech Synthesis of Low-Resource Languages 23 Mar 2023	The provided paper is about exploring solutions for text-to-speech synthesis of low-resource languages. It does not discuss the state of the art for speech-to-text.
Open access•Posted Content•DOI EfficientSpeech: An On-Device Text to Speech Model Andreas Kammer 23 May 2023	The provided paper is about an efficient on-device text to speech model called EfficientSpeech. It does not mention the state of the art for speech to text.
Proceedings Article•DOI Natural Text-to-Speech Synthesis by Conditioning Spectrogram Predictions from Transformer Network on WaveGlow Vocoder G Sanjay, K C Sooraj, Deepak Mishra - Show less +2 more 14 Nov 2020 2 Citations	The provided paper is about improving the performance of Text-to-Speech (TTS) synthesis using a modified model. It does not mention the state of the art for speech to text.
Open access•Journal Article•DOI A Survey on “Text-to-Speech Systems for Real-Time Audio Synthesis” Mrunalinee Patole, Akhilesh Pandey, Kaustubh Bhagwat, Mukesh Vaishnav, Salikram Chadar - Show less +4 more 10 Jun 2021 1 Citations	The provided paper is about text-to-speech systems, not speech-to-text. The paper does not provide information about the state of the art for speech-to-text.

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Why machine learning is useful for speech to text?5 answersMachine learning is beneficial for speech-to-text applications due to its ability to analyze and interpret spoken language accurately. By utilizing machine learning algorithms, such as neural networks and Support Vector Machines (SVM), speech data can be efficiently processed and converted into editable text. Additionally, machine learning techniques enable the recognition of various speech patterns and nuances, enhancing the accuracy of transcriptions. These methods are crucial in applications like text reading for the visually impaired and automatic speech recognition for different languages. Furthermore, machine learning plays a vital role in identifying hate speech in spoken or written form, contributing to the maintenance of a respectful online environment. Overall, machine learning's adaptability and efficiency make it a valuable tool for speech-to-text conversion and content analysis.

What is the latest state-of-the-art performance of text embeddings?4 answersThe latest state-of-the-art performance of text embeddings is achieved by the E5 model, which is a family of text embeddings that transfer well to a wide range of tasks. E5 is trained in a contrastive manner with weak supervision signals from a curated large-scale text pair dataset called CCPairs. It can be used as a general-purpose embedding model for tasks such as retrieval, clustering, and classification. E5 achieves strong performance in both zero-shot and fine-tuned settings, outperforming the strong BM25 baseline on the BEIR retrieval benchmark without using any labeled data. When fine-tuned, E5 obtains the best results on the MTEB benchmark, beating existing embedding models with 40x more parameters. Citations:: Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, F. Zhang Wei: Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Marietta Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, Tao Yu

Speech to text applications?5 answersSpeech-to-text applications have been studied in various contexts. One study investigated the effectiveness of using speech-to-text recognition (STR) technology to enhance learning and concentration during meditation. The study found that STR-texts had a positive effect on learning performance, attention, and meditation. Students in the experimental group, who received both verbal (speech) and visual (STR-texts) instructional content, had more positive perceptions of STR-texts for learning. Another study applied STR technology during teaching and learning activities, including lectures and seminars. The study aimed to investigate participants' perceptions of STR technology and its effectiveness on learning. The results showed positive perceptions and provided implications for using STR-texts effectively. These studies highlight the potential benefits of speech-to-text applications in enhancing learning and communication in various settings.

What are some of the challenges of speech-to-text in Brazil?5 answersOne of the challenges of speech-to-text in Brazil is the difficulty faced by students when listening to English outside the classrooms. Another challenge is the dominance of American English in English language teaching textbooks, which can create institutional and pedagogical challenges for non-American-English-speaking teachers of English. Additionally, there are challenges in providing adequate health and educational services to children with autism spectrum disorders (ASDs) and their families in Brazil. Furthermore, speech impaired Brazilian adults face challenges and barriers in their sexuality, social, and work-related relationships, which can be mitigated through the use of Augmentative and Alternative Communication (AAC). Lastly, teaching Portuguese in basic education faces challenges in adapting to the dynamics of linguistic processes through digital information and communication technologies (TDIC).

What is speech-to-text technology, and what are its applications?5 answersSpeech-to-text technology is a system that converts spoken language into written text. It has various applications in different fields. In the field of computer science and engineering, speech-to-text technology is used to develop text-to-speech systems that convert written text into spoken language. This technology is particularly useful for blind individuals who rely on sound-based information. In the education sector, speech-to-text technology has been used to improve the writing skills of children with special educational needs and disabilities (SEND). It has also been applied in language learning tasks, such as dictogloss exercises, to enhance listening skills and vocabulary acquisition. Additionally, speech-to-text technology has been explored in the domain of Human-Computer Interaction (HCI) to improve text input methods and error correction. Furthermore, it has been utilized in text summarization systems and deep learning-based text analysis for various applications.

How has speech to text technology been used in foreign language education in the literature?3 answersSpeech-to-text technology has been used in foreign language education to support pronunciation and prosody training. The use of this technology allows for the analysis and recognition of non-native speech, detecting pronunciation and prosodic errors, and providing multimodal feedback. The integration of speech technology into intelligent tutoring systems has been explored, with a focus on individualized learning courses and segmental features. The development of software and systems, such as the EURONOUNCE project, aims to create tools that can assist learners in improving their pronunciation and prosody in specific language pairs. These systems provide opportunities for learners to receive personalized feedback and engage in targeted practice to enhance their language skills. The literature highlights the potential of speech-to-text technology in supporting foreign language education and promoting effective pronunciation and prosody training.

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