A complete list of live websites using the Dynamic Text Spinner technology, compiled through global website indexing conducted by WebTechSurvey.

Text-conditioned human motion generation has experienced significant advancements with diffusion models trained on extensive motion capture data and corresponding textual annotations. However, extending such success to 3D dynamic human-object interaction (HOI) generation faces notable challenges, primarily due to the lack of large-scale interaction data and comprehensive descriptions that align with these interactions.

The Speech-to-Text API Market size is expected to reach USD 12.8 billion in 2024 registering a CAGR of 14.8. This Speech-to-Text API Market report analyzes segmentation, growth trends, demand patterns, and competitive positioning.

No description is available. Visit https://dataone.org/datasets/doi%3A10.18739%2FA2J678X27 for complete metadata about this dataset.

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Text-To-Speech Market Size 2025-2029

The text-to-speech market size is valued to increase by USD 3.99 billion, at a CAGR of 14.1% from 2024 to 2029. Rising demand for voice-enabled devices will drive the text-to-speech market.

Major Market Trends & Insights

North America dominated the market and accounted for a 43% growth during the forecast period.
By Language - English segment was valued at USD 1.34 billion in 2023
By Technology - Neural TTS segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 176.25 million
Market Future Opportunities: USD 3987.20 million
CAGR from 2024 to 2029 : 14.1%

Market Summary

The Text-to-Speech (TTS) market is experiencing significant growth due to the increasing popularity of voice-enabled devices and the development of advanced AI-based TTS models. These technologies are revolutionizing various industries by enhancing accessibility, improving operational efficiency, and ensuring regulatory compliance. For instance, in the supply chain sector, TTS technology is being used to automate warehouse operations, enabling real-time communication between workers and systems. This results in increased productivity and reduced errors. A recent study revealed that implementing TTS technology in a warehouse setting led to a 15% increase in order fulfillment accuracy. Moreover, the regulatory landscape is pushing businesses towards adopting TTS technology for compliance purposes.
In the financial sector, for example, TTS is used to read out sensitive financial information to customers, ensuring data privacy and security. This not only improves customer experience but also reduces the risk of human error. The development of AI-based TTS models is a major trend in the market, as they offer more natural and human-like voices. These models use Deep Learning algorithms to understand context and intonation, making them increasingly indistinguishable from human speech. Despite these advantages, challenges remain, including the need for continuous improvement in speech recognition accuracy and the high cost of implementing TTS solutions.
However, as the technology matures and becomes more accessible, it is expected to become a standard feature in various applications, from virtual assistants to Industrial Automation systems.

What will be the Size of the Text-To-Speech Market during the forecast period?

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How is the Text-To-Speech Market Segmented ?

The text-to-speech industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Language

  English
  Chinese
  Spanish
  Others


Technology

  Neural TTS
  Concatenative TTS
  Formant-based TTS


Type

  Natural voices
  Synthetic voices


End-user

  Automotive and transportation
  Healthcare
  Consumer Electronics
  Finance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Language Insights

The english segment is estimated to witness significant growth during the forecast period.

English continues to dominate the dynamic text-to-speech (TTS) market, driven by its extensive use in business, education, media, and technology sectors worldwide. TTS solutions for English are characterized by a diverse array of voice options, including American, British, and Australian accents. These systems cater to various speaking styles, ranging from formal and instructional to conversational and expressive. The English TTS market's growth is fueled by the increasing demand for applications such as virtual assistants, customer service platforms, e-learning modules, and accessibility tools. These domains rely heavily on English-language voice synthesis, reflecting both the global reach of the language and the technological advancements supporting it.

This growth is driven by ongoing activities, including the integration of advanced technologies like Natural Language Processing, voice cloning, and neural text-to-speech, as well as evolving patterns in stress modeling, speech quality metrics, and intonation control. TTS engines employ techniques such as unit selection synthesis, prosody modeling, and parametric synthesis, as well as neural vocoder and deep learning TTS, to deliver increasingly natural and expressive speech.

Additionally, TTS customization features like accent adaptation, emotional expression, and speech rate control cater to specific user needs. The TTS market's continuous evolution is further characterized by advancements in text processing, waveform generation, and speech coding, as well as

The global Text Analysis API Tool market is poised for robust expansion, projected to reach an estimated market size of USD 4,500 million in 2025 and surge to approximately USD 9,500 million by 2033, exhibiting a Compound Annual Growth Rate (CAGR) of 10.0% during the forecast period. This significant growth is fueled by the escalating need for businesses across all sectors to extract actionable insights from the vast and ever-increasing volume of unstructured text data. Key drivers include the burgeoning adoption of AI and machine learning technologies, the demand for enhanced customer experience management, and the imperative for improved business intelligence and competitive analysis. SMEs and large enterprises alike are increasingly leveraging these tools to understand customer sentiment, automate repetitive tasks, identify emerging trends, and make data-driven decisions, thereby optimizing operations and driving revenue. The market landscape is characterized by rapid technological advancements and a dynamic competitive environment. Leading players like IBM Watson Studio, Assembly AI, and Google Cloud Natural Language AI are at the forefront of innovation, offering sophisticated solutions that cater to diverse application needs, from sentiment analysis and topic modeling to entity recognition and intent classification. While the cloud-based segment continues to dominate due to its scalability and accessibility, on-premises solutions retain relevance for organizations with stringent data security and privacy requirements. Emerging trends such as the integration of Natural Language Processing (NLP) with Generative AI for more nuanced text understanding and generation, along with the increasing focus on industry-specific text analysis solutions, are expected to further propel market growth. However, challenges related to data privacy concerns, the complexity of natural language understanding, and the need for skilled professionals may pose minor restraints, yet are being effectively addressed through continuous R&D and market evolution. This report delves into the dynamic Text Analysis API Tool market, forecasting significant growth and profound impact across industries. From 2019 to 2033, this market is poised for a transformative journey, with the Base Year of 2025 serving as a pivotal point for estimating its future trajectory. The Forecast Period (2025-2033) will witness an explosion in adoption, building upon the foundational insights from the Historical Period (2019-2024). With an estimated market value in the hundreds of millions by 2025, and projections reaching billions by 2033, this technology is no longer a niche offering but a cornerstone of modern business intelligence.

Explore the dynamic Text Editor Tool market: size, growth drivers, key trends, and forecasts. Discover insights into cloud-based vs. on-premises solutions and regional market shares.

This dataset consists of articles title's, abstract's and introduction's that have been paraphrased using the GPT-3 language model. The original articles were selected from Assoc. Prof. Mehmet Erkut Erdem's Google Scholar page and rewritten in order to maintain their meaning while changing the wording and structure using GPT-3 language model API. The resulting dataset is useful for natural language processing tasks such as text summarization, machine translation, and data augmentation.

We manually copy the articles title's abstract's and introduction's parts, and then paste them into Google Sheets. Using OpenAI GPT-3 API's, we code a script to automatically send a API request using our inputs. After that, using pandas library, remove the new lines and save it to the .csv file.

We only use Open Access articles to create this dataset.

Overall, this dataset represents a valuable resource for researchers and practitioners in the field of natural language processing, as it provides a diverse and high-quality source of paraphrased articles that can be used for a range of NLP tasks.

Supplementary Material (2.33 MB DOC)

contains a CSV file with the list of words in the sample that survived the filters described in the article

Sora-2 is OpenAI’s next-generation AI video generation model. It supports text or image input to create high-quality dynamic clips, featuring intelligent camera movement, lighting and color mood control, and a natural, smooth visual experience. It helps creators easily produce immersive video content.

This respository includes two datasets, a Document-Term Matrix and associated metadata, for 17,493 New York Times articles covering protest events, both saved as single R objects.

These datasets are based on the original Dynamics of Collective Action (DoCA) dataset (Wang and Soule 2012; Earl, Soule, and McCarthy). The original DoCA datset contains variables for protest events referenced in roughly 19,676 New York Times articles reporting on collective action events occurring in the US between 1960 and 1995. Data were collected as part of the Dynamics of Collective Action Project at Stanford University. Research assistants read every page of all daily issues of the New York Times to find descriptions of 23,624 distinct protest events. The text for the news articles were not included in the original DoCA data.

We attempted to recollect the raw text in a semi-supervised fashion by matching article titles to create the Dynamics of Collective Action Corpus. In addition to hand-checking random samples and hand-collecting some articles (specifically, in the case of false positives), we also used some automated matching processes to ensure the recollected article titles matched their respective titles in the DoCA dataset. The final number of recollected and matched articles is 17,493.

We then subset the original DoCA dataset to include only rows that match a recollected article. The "20231006_dca_metadata_subset.Rds" contains all of the metadata variables from the original DoCA dataset (see Codebook), with the addition of "pdf_file" and "pub_title" which is the title of the recollected article (and may differ from the "title" variable in the original dataset), for a total of 106 variables and 21,126 rows (noting that a row is a distinct protest events and one article may cover more than one protest event).

Once collected, we prepared these texts using typical preprocessing procedures (and some less typical procedures, which were necessary given that these were OCRed texts). We followed these steps in this order: We removed headers and footers that were consistent across all digitized stories and any web links or HTML; added a single space before an uppercase letter when it was flush against a lowercase letter to its right (e.g., turning "JohnKennedy'' into "John Kennedy''); removed excess whitespace; converted all characters to the broadest range of Latin characters and then transliterated to ``Basic Latin'' ASCII characters; replaced curly quotes with their ASCII counterparts; replaced contractions (e.g., turned "it's'' into "it is''); removed punctuation; removed capitalization; removed numbers; fixed word kerning; applied a final extra round of whitespace removal.

We then tokenized them by following the rule that each word is a character string surrounded by a single space. At this step, each document is then a list of tokens. We count each unique token to create a document-term matrix (DTM), where each row is an article, each column is a unique token (occurring at least once in the corpus as a whole), and each cell is the number of times each token occurred in each article. Finally, we removed words (i.e., columns in the DTM) that occurred less than four times in the corpus as a whole or were only a single character in length (likely orphaned characters from the OCRing process). The final DTM has 66,552 unique words, 10,134,304 total tokens and 17,493. The "20231006_dca_dtm.Rds" is a sparse matrix class object from the Matrix R package.

In R, use the load() function to load the objects dca_dtm and dca_meta. To associate the dca_meta to the dca_dtm , match the "pdf_file" variable indca_meta to the rownames of dca_dtm.

Explore the dynamic Text-to-Video Generation Model market, driven by AI innovation in media, advertising, and gaming. Discover market size, CAGR, key drivers, trends, and regional insights for lucrative investment opportunities.

Explore the dynamic Text Mining market forecast, key drivers, and trends driving its expansion to USD 56 billion by 2033. Discover insights into data analysis, fraud detection, and CRM applications.

perturbed toxic text dataset (from Jigsaw dataset), managed by the types of perturbations. There are 9 types of perturbations:

insert repeat maskword homoglyph swap remove abbrs./slangs distract words distract sentences

You also can access to Github: https://github.com/khk-abc/DynEscape---Dynamic-Escape The paper has been published. Please cite the following paper if you would like to use this dataset. @article{kang2026developing, title={Developing continuous toxicity detection against… See the full description on the dataset page: https://huggingface.co/datasets/kkk3lll/DynEscape.

R metric

The statistics show significant differences in the model parameters k0, kt and kp between Chinese and English texts, indicating differences in the dynamic process underlying the language structure, words organization and the occurrence of new words with text growth.

Currently, text-driven generation models are booming in video editing with their compelling results. However, for the face-centric text-to-video generation, challenges remain severe as a suitable dataset with high-quality videos and highly-relevant texts is lacking. In this work, we present a large-scale, high-quality, and diverse facial text-video dataset, CelebV-Text, to facilitate the research of facial text-to-video generation tasks. CelebV-Text contains 70,000 in-the-wild face video clips covering diverse visual content. Each video clip is paired with 20 texts generated by the proposed semi-auto text generation strategy, which is able to describe both the static and dynamic attributes precisely. We make comprehensive statistical analysis on videos, texts, and text-video relevance of CelebV-Text, verifying its superiority over other datasets. Also, we conduct extensive self-evaluations to show the effectiveness and potential of CelebV-Text. Furthermore, a benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task.

Climate change is amplified in the Polar Regions. Polar amplification is captured via space and airborne remote sensing, in-situ measurement, and climate modeling. Beyond the rich literature that documents changing Polar regions, each method of Polar-data collection produces a diverse set of data types, ranging from text-based metadata to more complex data structures (e.g. HDF, NetCDF, GRIB). Because finding these data is often a primary challenge in scientific discovery, inclusion of the Polar dataset in TREC-DD would help advance science through data discovery and provide TREC-DD a new challenge in in the realm of search relevancy.

Dataset Description:

This dataset is a collection of web crawls from three primary sources:

Dr. Chris Mattmann's crawl of ADE, performed at the Open Science Codefest and at the NSF DataViz Hackathon for Polar CyberInfrastructure Dr. Mattmann's student Angela Wang, contributed 3 datasets: 2 crawls of ACADIS and one of NASA AMD. Dr. Mattmann's CSCI 572 Course at USC, students submitted 13 individual crawls of NASA ACADIS, NSIDC ADE, and AMD. Each web crawl used Apache Nutch as the core framework for web crawling and Apache Tika as the main content detection and extraction framework. Nutch is a distributed search engine that runs on top of Apache Hadoop. Apache Tika is an open source framework for metadata exploration, automatic text mining, and information retrieval.

Web crawls were focused on three polar data repositories: the National Science Foundation Advanced Cooperative Arctic Data and Information System (ACADIS), the National Snow and Ice Data Center (NSIDC) Arctic Data Explorer (ADE), and the National Aeronautics and Space Administration Antarctic Master Directory (AMD).

The finished Polar dataset is composed of 17 distinct web crawls, containing 1,741,530 records (158 GB) across the three Polar science data repositories, which themselves are largely uncoordinated.