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Facebook AI Wav2Vec 2.0: Automatic Speech Recognition From 10 Minute Sample


Speech-to-text applications have never been so plentiful, popular or powerful, with researchers' pursuit of ever-better automatic speech recognition (ASR) system performance bearing fruit thanks to huge advances in machine learning technologies and the increasing availability of large speech datasets. Current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance. However, a lack of transcribed audio data for the less widely spoken of the world's 7,000 languages and dialects makes it difficult to train robust speech recognition systems in this area. To help ASR development for such low-resource languages and dialects, Facebook AI researchers have open-sourced the new wav2vec 2.0 algorithm for self-supervised language learning. The paper Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations claims to "show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler." A Facebook AI tweet says the new algorithm can enable automatic speech recognition models with just 10 minutes of transcribed speech data.

Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI Artificial Intelligence

Today's voice assistants are fairly limited in their conversational abilities and we look forward to their evolution toward The ambition of AI research is not solely to create intelligent increasing capability. Smart speakers and voice applications artifacts that have the same capabilities as people; are a result of the foundational research that has come to we also seek to enhance our intelligence and, in particular, life in today's consumer products. These systems can complete to build intelligent artifacts that assist in our intellectual simple tasks well: send and read text messages; answer activities. Intelligent assistants are a central component basic informational queries; set timers and calendar of a long history of using computation to improve human entries; set reminders, make lists, and do basic math calculations; activities, dating at least back to the pioneering work control Internet-of-Things-enabled devices such of Douglas Engelbart (1962). Early examples of intelligent as thermostats, lights, alarms, and locks; and tell jokes and assistants include sales assistants (McDermott 1982), stories (Hoy 2018). Although voice assistants have greatly scheduling assistants (Fox and Smith 1984), intelligent tutoring improved in the last few years, when it comes to more complicated systems (Grignetti, Hausmann, and Gould,Anderson, routines, such as rescheduling appointments in a Boyle, and Reiser 1975, 1985), and intelligent assistants for calendar, changing a reservation at a restaurant, or having a software development and maintenance (Winograd, Kaiser, conversation, we are still looking forward to a future where Feiler, and Popovich 1973, 1988). More recent examples assistants are capable of completing these tasks. Are today's of intelligent assistants are e-commerce assistants (Lu and voice systems "conversational"? We say that intelligent assistants Smith 2007), meeting assistants (Tür et al. 2010), and systems are conversational if they are able to recognize and that offer the intelligent capabilities of modern search respond to input; to generate their own input; to deal with

Complete Machine Learning with R Studio - ML for 2020


Online Courses Udemy - Complete Machine Learning with R Studio - ML for 2020, Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio 4.1 (41 ratings), Created by Start-Tech Academy, English [Auto-generated] Preview this Udemy course -. GET COUPON CODE Description In this course we will learn and practice all the services of AWS Machine Learning which is being offered by AWS Cloud. There will be both theoretical and practical section of each AWS Machine Learning services.This course is for those who loves machine learning and would build application based on cognitive computing, AI and ML. You could integrate these services in your Web, Android, IoT, Desktop Applications like Face Detection, ChatBot, Voice Detection, Text to custom Speech (with pitch, emotions, etc), Speech to text, Sentimental Analysis on Social media or any textual data. Machine Learning Services like- Amazon Sagemaker to build, train, and deploy machine learning models at scale Amazon Comprehend for natural Language processing and text analytics Amazon Lex for conversational interfaces for your applications powered by the same deep learning technologies as Alexa Amazon Polly to turn text into lifelike speech using deep learning Object and scene detection,Image moderation,Facial analysis,Celebrity recognition,Face comparison,Text in image and many more Amazon Transcribe for automatic speech recognition Amazon Translate for natural and accurate language translation As Machine learning and cloud computing are trending topic and also have lot of job opportunities If you have interest in machine learning as well as cloud computing then this course for you.

Dialogue-based simulation for cultural awareness training Artificial Intelligence

Existing simulations designed for cultural and interpersonal skill training rely on pre-defined responses with a menu option selection interface. Using a multiple-choice interface and restricting trainees' responses may limit the trainees' ability to apply the lessons in real life situations. This systems also uses a simplistic evaluation model, where trainees' selected options are marked as either correct or incorrect. This model may not capture sufficient information that could drive an adaptive feedback mechanism to improve trainees' cultural awareness. This paper describes the design of a dialogue-based simulation for cultural awareness training. The simulation, built around a disaster management scenario involving a joint coalition between the US and the Chinese armies. Trainees were able to engage in realistic dialogue with the Chinese agent. Their responses, at different points, get evaluated by different multi-label classification models. Based on training on our dataset, the models score the trainees' responses for cultural awareness in the Chinese culture. Trainees also get feedback that informs the cultural appropriateness of their responses. The result of this work showed the following; i) A feature-based evaluation model improves the design, modeling and computation of dialogue-based training simulation systems; ii) Output from current automatic speech recognition (ASR) systems gave comparable end results compared with the output from manual transcription; iii) A multi-label classification model trained as a cultural expert gave results which were comparable with scores assigned by human annotators.

Top 10 Automatic Speech Recognition Tools That'll Relieve You Of The Keyboard


Speech recognition is the process of decoding human voices and is a part of machine learning. Organisations are implementing Automatic Speech Recognition (ASR) technology to create documents without touching the keyboard, controlling devices, and other similar tasks. In this article, we list down 10 speech-to-text services which can be used for various applications. Amazon Transcribe is an Automatic Speech recognition (ASR) service which converts speech to text quickly. The features of this service include easy-to-read transcriptions, streaming transcription, timestamp generation, custom vocabulary, multiple speaker recognition, and channel identification.

Avaya Conversational Intelligence: A Real-Time System for Spoken Language Understanding in Human-Human Call Center Conversations Machine Learning

Avaya Conversational Intelligence (ACI) is an end-to-end, cloud-based solution for real-time Spoken Language Understanding for call centers. It combines large vocabulary, real-time speech recognition, transcript refinement, and entity and intent recognition in order to convert live audio into a rich, actionable stream of structured events. These events can be further leveraged with a business rules engine, thus serving as a foundation for real-time supervision and assistance applications. After the ingestion, calls are enriched with unsupervised keyword extraction, abstractive summarization, and business-defined attributes, enabling offline use cases, such as business intelligence, topic mining, full-text search, quality assurance, and agent training. ACI comes with a pretrained, configurable library of hundreds of intents and a robust intent training environment that allows for efficient, cost-effective creation and customization of customer-specific intents.

A 20-Year Community Roadmap for Artificial Intelligence Research in the US Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

Survey on Evaluation Methods for Dialogue Systems Artificial Intelligence

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class.

Realizing Petabyte Scale Acoustic Modeling Machine Learning

Large scale machine learning (ML) systems such as the Alexa automatic speech recognition (ASR) system continue to improve with increasing amounts of manually transcribed training data. Instead of scaling manual transcription to impractical levels, we utilize semi-supervised learning (SSL) to learn acoustic models (AM) from the vast firehose of untranscribed audio data. Learning an AM from 1 Million hours of audio presents unique ML and system design challenges. We present the design and evaluation of a highly scalable and resource efficient SSL system for AM. Employing the student/teacher learning paradigm, we focus on the student learning subsystem: a scalable and robust data pipeline that generates features and targets from raw audio, and an efficient model pipeline, including the distributed trainer, that builds a student model. Our evaluations show that, even without extensive hyper-parameter tuning, we obtain relative accuracy improvements in the 10 to 20$\%$ range, with higher gains in noisier conditions. The end-to-end processing time of this SSL system was 12 days, and several components in this system can trivially scale linearly with more compute resources.

while True: learn() on Steam


About This Game ABOUT THE GAME while True: learn() is a puzzle/simulation game about even more puzzling stuff: machine learning, neural networks, big data and AI. In this game, you play as a coder who accidentally found out that their cat is extremely good at coding, but not as good at speaking human language. Now this coder (it's you!) must learn all there is to know about machine learning and use visual programming to build a cat-to-human speech recognition system. Learn how machine learning works in real life The game is loosely based on real-life machine learning technologies: from goofy Expert Systems to mighty Recurrent Neural Networks, capable of predicting the future. Don't worry: it all plays out as a puzzle game.