Text summarization is the process of condensing large amounts of text into shorter, more concise summaries while retaining the most important information. Text summarization has become increasingly important due to the rapid growth of data in various domains, such as news, social media, and education. Automatic text summarization techniques have been developed to help users quickly understand the main ideas of a document without having to read the entire text. These techniques can be broadly categorized into extractive and abstractive methods. Extractive methods select important sentences from the original text to form a summary, while abstractive methods generate new sentences that convey the main ideas of the text. Recent research in text summarization has explored various approaches, including neural networks, hierarchical models, and query-based methods. One study proposed a hierarchical end-to-end model for jointly improving text summarization and sentiment classification, treating sentiment classification as a further 'summarization' of the text. Another study focused on query-based text summarization, which condenses text data into a summary guided by user-provided query information. This approach has been studied for a long time, but a systematic survey of the existing work is still lacking. Semantic relevance is another important aspect of text summarization. A study introduced a Semantic Relevance Based neural model to encourage high semantic similarity between source texts and summaries. This model uses a gated attention encoder to represent the source text and a decoder to produce the summary representation, maximizing the similarity score between the representations during training. Evaluating the quality of automatic text summarization remains a challenge. One recent study proposed a reference-less evaluation system that measures the quality of text summarization models based on factual consistency, comprehensiveness, and compression rate. This system is the first to evaluate text summarization models based on factuality, information coverage, and compression rate. Practical applications of text summarization include news summarization, customer review summarization, and summarization of scientific articles. For example, a company could use text summarization to analyze customer feedback and identify common themes or issues. This information could then be used to improve products or services. In conclusion, text summarization is a valuable tool for managing the ever-growing amount of textual data. By condensing large amounts of text into shorter, more concise summaries, users can quickly understand the main ideas of a document without having to read the entire text. As research in this field continues to advance, we can expect to see even more accurate and efficient text summarization techniques in the future.
Text-to-Speech (TTS)
What is Text-to-Speech (TTS) technology?
Text-to-Speech (TTS) technology is a field of artificial intelligence that focuses on converting written text into natural-sounding, intelligible speech. It has various applications in industries such as assistive technologies, virtual assistants, and language learning. Recent advancements in neural TTS, powered by deep learning, have significantly improved the quality of synthesized speech.
What are the key components of neural TTS systems?
Neural TTS systems typically consist of three main components: text analysis, acoustic models, and vocoders. Text analysis involves converting the input text into a phonetic representation, which is then used by the acoustic models to generate speech features. Finally, vocoders synthesize the speech waveform from these features, resulting in the final audio output.
What are some advanced topics in neural TTS research?
Advanced topics in neural TTS research include fast TTS, low-resource TTS, robust TTS, expressive TTS, and adaptive TTS. These areas focus on improving the efficiency, performance, and versatility of TTS systems, making them more suitable for a wide range of applications and environments.
How does the Low-Rank Tensor-Train Deep Neural Network (LR-TT-DNN) approach work?
The Low-Rank Tensor-Train Deep Neural Network (LR-TT-DNN) is a recent approach in neural TTS research that combines a Convolutional Neural Network (CNN) with a low complexity hybrid tensor network. This method aims to balance the trade-offs between model complexity and practical performance, resulting in models with fewer parameters that can outperform their counterparts in tasks such as speech enhancement and spoken command recognition.
What are some practical applications of TTS technology?
Three practical applications of TTS technology include: 1. Assistive technologies: TTS can help individuals with visual impairments or reading difficulties by converting text into speech, making digital content more accessible. 2. Virtual assistants: TTS is a crucial component in voice-based virtual assistants, such as Siri, Alexa, and Google Assistant, enabling them to provide spoken responses to user queries. 3. Audiobooks and language learning: TTS can be used to generate audiobooks or language learning materials, providing users with an engaging and interactive learning experience.
How has Microsoft utilized neural TTS in their products?
Microsoft has leveraged neural TTS technology to improve the quality of synthesized speech in their products, such as Cortana and Microsoft Translator. By using deep learning techniques, their TTS system generates more natural-sounding speech, enhancing user experience and satisfaction.
What is the most realistic TTS voice?
The most realistic TTS voices are typically generated by advanced neural TTS systems, which leverage deep learning techniques to produce natural-sounding speech. Examples of such systems include Google's Tacotron, Microsoft's neural TTS, and Amazon's Polly. The perceived realism of a TTS voice may vary depending on the listener and the specific use case.
How do I use Google TTS?
Google TTS can be accessed through the Google Cloud Text-to-Speech API, which allows developers to integrate TTS functionality into their applications. To use Google TTS, you need to create a Google Cloud Platform account, enable the Text-to-Speech API, and obtain an API key. You can then use this key to make requests to the API, providing the input text and desired voice settings to generate speech audio.
How do I convert text to speech audio?
To convert text to speech audio, you can use a TTS software or service, such as Google TTS, Microsoft's neural TTS, or Amazon Polly. These services typically provide APIs or user interfaces that allow you to input text and select voice settings, such as language, gender, and speaking rate. The TTS system then processes the text and generates an audio file or streams the synthesized speech directly.
Is TTS Reader free?
TTS Reader is a term that can refer to various text-to-speech applications or services. Some TTS Readers are free, while others may require a subscription or a one-time purchase. Examples of free TTS Readers include Google TTS (with limited usage), Microsoft's built-in TTS functionality in Windows, and some open-source TTS projects like eSpeak. It's essential to check the specific TTS Reader you're interested in for pricing and usage details.
Text-to-Speech (TTS) Further Reading
1.Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing http://arxiv.org/abs/2203.06031v1 Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Javier Tejedor2.Ideals in the convolution algebra of periodic distributions http://arxiv.org/abs/2304.07285v1 Amol Sasane3.Determination of a Type of Permutation Trinomials over Finite Fields http://arxiv.org/abs/1309.3530v1 Xiang-dong Hou4.On Global $\mathcal P$-Forms http://arxiv.org/abs/1405.4816v1 Xiang-dong Hou5.A Survey on Neural Speech Synthesis http://arxiv.org/abs/2106.15561v3 Xu Tan, Tao Qin, Frank Soong, Tie-Yan Liu6.The signed enhanced principal rank characteristic sequence http://arxiv.org/abs/1612.08940v2 Xavier Martínez-Rivera7.Proof of a Conjecture on Permutation Polynomials over Finite Fields http://arxiv.org/abs/1304.2254v1 Xiang-dong Hou8.Nonlinear PDE aspects of the tt* equations of Cecotti and Vafa http://arxiv.org/abs/1010.1889v1 Martin A. Guest, Chang-Shou Lin9.Disordered vectors in R: introducing the disordR package http://arxiv.org/abs/2210.03856v2 Robin K. S. Hankin10.Learning Speaker Embedding from Text-to-Speech http://arxiv.org/abs/2010.11221v1 Jaejin Cho, Piotr Zelasko, Jesus Villalba, Shinji Watanabe, Najim DehakExplore More Machine Learning Terms & Concepts
Text Summarization Thompson Sampling Thompson Sampling: A Bayesian approach to balancing exploration and exploitation in online learning tasks. Thompson Sampling is a popular Bayesian method used in online learning tasks, particularly in multi-armed bandit problems, to balance exploration and exploitation. It works by allocating new observations to different options (arms) based on the posterior probability that an option is optimal. This approach has been proven to achieve sub-linear regret under various probabilistic settings and has shown strong empirical performance across different domains. Recent research in Thompson Sampling has focused on addressing its challenges, such as computational demands in large-scale problems and the need for accurate model fitting. One notable development is Bootstrap Thompson Sampling (BTS), which replaces the posterior distribution used in Thompson Sampling with a bootstrap distribution, making it more scalable and robust to misspecified error distributions. Another advancement is Regenerative Particle Thompson Sampling (RPTS), which improves upon Particle Thompson Sampling by regenerating new particles in the vicinity of fit surviving particles, resulting in uniform improvement and flexibility across various bandit problems. Practical applications of Thompson Sampling include adaptive experimentation, where it has been compared to other methods like Tempered Thompson Sampling and Exploration Sampling. In most cases, Thompson Sampling performs similarly to random assignment, with its relative performance depending on the number of experimental waves. Another application is in 5G network slicing, where RPTS has been used to effectively allocate resources. Furthermore, Thompson Sampling has been extended to handle noncompliant bandits, where the agent's chosen action may not be the implemented action, and has been shown to match or outperform traditional Thompson Sampling in both compliant and noncompliant environments. In conclusion, Thompson Sampling is a powerful and flexible method for addressing online learning tasks, with ongoing research aimed at improving its scalability, robustness, and applicability to various problem domains. Its connection to broader theories, such as Bayesian modeling of policy uncertainty and game-theoretic analysis, further highlights its potential as a principled approach to adaptive sequential decision-making and causal inference.
