Forecast of Automatic Speech Recognition Technology
2000年の節目ということで、 10年先をみすえた音声認識技術の予測を行ってみたいと思います。 これは、情報処理学会の 音声言語情報処理(SLP)研究会 の 7月14・15日の研究会 で企画したもので、主に著者が興味のある項目について質問を設定しました。 調査項目は、(1)応用面での展開、(2)技術面での展開、(3)社会の環境 から構成して います。 研究会当日までに50名の方から回答を寄せて頂きました。 この結果を報告すると共に、 中村哲(ATR)、西村雅史(IBM)、武田一哉(名大)の各氏にコメンテータ／ パネリストをつとめて頂き、討論を行いました。 回答集計結果 予稿の段階で頂いたコメント 研究会当日に配布した資料 パネル討論書き起こし 河原達也(京大) kawahara@kuis.kyoto-u.ac.jp
SpecAugment: A New Data Augmentation Method for Automatic Speech Recognition
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
Speech Recognition
Download demo project - 2016.9 KB Download source - 64.9 KB Introduction This is part of a larger project on speech recognition we developed at ORT Braude college. The aim of the project is to activate programs on your desktop or panel by voice. Motivation We planned to make some common tasks that every user does on his/her computer (opening/ closing programs, editing texts, calculating) possible
Places: A 10 million Image Database for Scene Recognition
The dataset is designed following principles of human visual cognition. Our goal is to build a core of visual knowledge that can be used to train artificial systems for high-level visual understanding tasks, such as scene context, object recognition, action and event prediction, and theory-of-mind inference. The semantic categories of are defined by their function: the labels represent the entry-l
The Netlab toolbox is designed to provide the central tools necessary for the simulation of theoretically well founded neural network algorithms and related models for use in teaching, research and applications development. It is extensively used in the MSc by Research in the Mathematics of Complex Systems. It consists of a toolbox of Matlab® functions and scripts based on the approach and techniq
CS231n Convolutional Neural Networks for Visual Recognition
(this page is currently in draft form) Visualizing what ConvNets learn Several approaches for understanding and visualizing Convolutional Networks have been developed in the literature, partly as a response the common criticism that the learned features in a Neural Network are not interpretable. In this section we briefly survey some of these approaches and related work. Visualizing the activation
A convolutional neural network achieves 99.26% accuracy on a modified NIST database of hand-written digits. Download the Neural Network demo project - 203 Kb (includes a release-build executable that you can run without the need to compile) Download a sample neuron weight file - 2,785 Kb (achieves the 99.26% accuracy mentioned above) Download the MNIST database - 11,594 Kb total for all four files
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this spac
Real-Time Recognition of Handwritten Chinese Characters Spanning a Large Inventory of 30,000 Characters
Real-Time Recognition of Handwritten Chinese Characters Spanning a Large Inventory of 30,000 Characters Handwriting recognition is more important than ever given the prevalence of mobile phones, tablets, and wearable gear like smartwatches. The large symbol inventory required to support Chinese handwriting recognition on such mobile devices poses unique challenges. This article describes how we me
Deep Learning Models for Human Activity Recognition
Human activity recognition, or HAR, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model. Recently, deep learning methods such as convolutional neural networks
Facial recognition identifies extremists storming the Capitol
Correction: An earlier version of this story incorrectly stated that XRVision facial recognition software identified Antifa members among rioters who stormed the Capitol Wednesday. XRVision did not identify any Antifa members. The Washington Times apologizes to XRVision for the error. Facial recognition software has identified neo-Nazis and other extremists as participants in Wednesday’s assault o
IBM Watson の Visual Recognition（ビジュアル・レコグニション）は、ディープ・ラーニングを使用して画像に写った様々なものを分析・認識してくれる画像認識サービスです。IBM Cloud のライト・アカウントで一定の範囲内であれば無料で使えます。Visual Recognition で便利なのは、オリジナルの機械学習モデルを WEBブラウザから簡単に作成できるところです。そこで今回は、Visual Recognition でオリジナルの機械学習モデル作成する手順をまとめてみました。 （2019年1月1日更新）Visual Recognition が Watson Studio に対応したため記事を更新しました。また、以前と比べてAPIの認証手順が簡単になっています。 IBM Cloudライト・アカウントの登録 IBM Cloud には、「ライト・アカウント」というク
Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC
GitHub - chongyangtao/Awesome-Scene-Text-Recognition: A curated list of resources dedicated to scene text localization and recognition
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"Named entities" redirects here. For HTML, XML, and SGML named entities, see List of XML and HTML character entity references. Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as p
In this experience report we encode a well specified, compact benchmark in four programming languages, namely C++, Java, Go, and Scala. The implementations each use the languages’ idiomatic container classes, looping constructs, and memory/object allocation schemes. It does not attempt to exploit specific language and runtime features to achieve maximum performance. This approach allows an almost fa
(These notes are currently in draft form and under development) Table of Contents: Transfer Learning Additional References Transfer Learning In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNe
Table of Contents: Introduction Visualizing the loss function Optimization Strategy #1: Random Search Strategy #2: Random Local Search Strategy #3: Following the gradient Computing the gradient Numerically with finite differences Analytically with calculus Gradient descent Summary Introduction In the previous section we introduced two key components in context of the image classification task: A (
Building an image recognition React app using ONNX.js ONNX.js is a JavaScript library by Microsoft for running ONNX models on browsers and on Node.js. The Open Neural Network Exchange (ONNX) is an open standard for representing machine learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose combinations that are best for them. ONNX is develop
Simple Transformers — Named Entity Recognition with Transformer Models
Simple Transformers — Named Entity Recognition with Transformer Models Simple Transformers is the “it just works” Transformer library. Use Transformer models for Named Entity Recognition with just 3 lines of code. Yes, really.
The Mysterious Noh Mask: Contribution of Multiple Facial Parts to the Recognition of Emotional Expressions
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About KeyLemon Gilles Florey and Yann Rodriguez founded KeyLemon in 2008 in Martigny (Switzerland), a region as renowned for its Alpine skiing and scenic beauty as for its entrepreneurial dynamism. They founded KeyLemon on the simple idea that your device should be able to recognize you in a natural and passive way, like a human does, rather than you having to constantly re-enter (and remember) a
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
■イベント ：第六回 全日本コンピュータビジョン勉強会 https://kantocv.connpass.com/event/205271/ ■登壇概要 タイトル：Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition 発表者： DSOC R&D研究員 内田 奏 ▼Twitter https://twitter.com/SansanRandD
Recognition of aerosol transmission of infectious agents: a commentary - BMC Infectious Diseases
Review Open Access Published: 31 January 2019 Recognition of aerosol transmission of infectious agents: a commentary Raymond Tellier1, Yuguo Li2, Benjamin J. Cowling3 & …Julian W. Tang4,5 Show authors BMC Infectious Diseases volume 19, Article number: 101 (2019) Cite this article Although short-range large-droplet transmission is possible for most respiratory infectious agents, deciding on whether
北海道大学 大学院情報科学研究科 CS専攻 情報知識ネットワーク研究室 鈴木康広 Pattern Recognition and Machine Learning 2. Probability Distribution 2.1 Binary Variables Pattern Recognition and Machine Learning 2. Probability Distribution, which had read in Information Knowledge Network lab 6 月 15 日 イントロダクション ベータ分布(Beta Distribution) ・ベータ分布の定義 ・過学習 ・ベータ分布の期待値と分散 ・ベイズ的ベータ分布 ・超パラメータ ・逐次学習 1 Pattern Recognition and Machine Learning 2. Pro
In this chapter, we'll make use of the image parsing library we developed in Chapter 10, Code case study: parsing a binary data format to build a barcode recognition application. Given a picture of the back of a book taken with a camera phone, we could use this to extract its ISBN number. The vast majority of packaged and mass-produced consumer goods sold have a barcode somewhere on them. Although
Everybody has had the experience of not recognising someone they know—changes in pose, illumination and expression all make the task tricky. So it’s not surprising that computer vision systems have similar problems. Indeed, no computer vision system matches human performance despite years of work by computer scientists all over the world. That’s not to say that face recognition systems are poor. F
GitHub - bitbanger/gogaku: Kanji recognition - implementation of Nei Kato's directional feature extraction algorithm
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Since the last dlib release, I've been working on adding easy to use deep metric learning tooling to dlib. Deep metric learning is useful for a lot of things, but the most popular application is face recognition. So obviously I had to add a face recognition example program to dlib. The new example comes with pictures of bald Hollywood action heroes and uses the provided deep metric model to identi
Yes officer, I saw the speed limit sign. I just didn’t see you. This is part 1 of a series about building a deep learning model to recognize traffic signs. It’s intended to be a learning experience, for myself and for anyone else who likes to follow along. There are a lot of resources that cover the theory and math of neural networks, so I’ll focus on the practical aspects instead. I’ll describe m
News Release 20110518 クウジット|マーカーレス認識|markerless image recognition|Sony|ソニー|拡張現実感| マーカー認識|marker image recognition|augmented reality|SLAM|環境認識|画像認識|3D|3次元| KART|Koozyt AR Technology|
クウジットは、ソニーが主催する「新しい拡張現実感(AR)技術とその応用」体験イベント(5/20-22)に企画・開発・運営協力しております。 本イベントでは、主にスマートフォン(Xperia™ arc)を用いた(*1)新しいAR体験デモが用意されています。 *1) プロトタイプのソフトウェアによるデモで実際の商品・サービスではご利用頂けません 日時：5月20日(金)～5月22日(日) 時間：11:00～19:00 場所：東京都中央区銀座5-3-1 ソニービル 8F(コミュニケーションゾーン OPUS) 料金：無料 主催：ソニー株式会社 協力：クウジット株式会社 クウジットは、今後もリアルとネットをつなぐ技術で、人々の生活をそっと後押しするようなサービスの実現を目指し、新しい体験価値を提案、創造してまいります。ぜひ銀座ソニービル 8F OPUSイベント会場までご来場ください。 以上
GitHub - mumumu/Titanium-Android-VoiceRecognition: Titanium Module for Voice Recognition on Android.
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ディープラーニングが分からなくてもいい感じに物体認識させるサービスを試す(iOS Swift + Bluemix Visual Recognition) - Qiita
BluemixのVisual Recognitionサービスを利用します。 このサービスの凄いところは、ディープラーニング・機械学習のなんたるかを理解していなくても、認識させたい物体の画像をzipにまとめて送信、サービス側でディープラーニングを行い、分類器を作成してくれます。 フリープランがあるので、無料で試せます。 Visual Recognitionの概要やサービス作成方法については、すでに良い記事があるのでこちら参照。 Version UpしたVisual Recognitionで、Watsonをトレーニングして独自の画像判別モデルを作る！ BluemixでWatson API のVisual Recognition を使う by curl BluemixでWatson API のVisual Recognition を使う by python サービスのドキュメントは こちら、制約
GitHub - TalAter/SpeechKITT: 🗣 A flexible GUI for Speech Recognition
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Intel Set-Top Box Uses Face Recognition to Target Ads to You [VIDEO]
Intel Set-Top Box Uses Face Recognition to Target Ads to You [VIDEO] [brightcove video="1681927065001" /] Intel has developed a new set-top box that will monitor who is watching TV at that moment, enabling advertisers to target the most appropriate ads to them. The box does this by using facial recognition technology. The box won't identify the specific person watching TV, but will be able to tell
![Intel Set-Top Box Uses Face Recognition to Target Ads to You [VIDEO]](https://cdn-ak-scissors.b.st-hatena.com/image/square/b72033bd98cafe7513893939d00ac31036b3b347/height=288;version=1;width=512/https%3A%2F%2Fhelios-i.mashable.com%2Fimagery%2Farchives%2F047drBvNh7YDurrv3ou8OBM%2Fhero-image.fill.size_1200x675.v1647021031.jpg)
Welcome! This website introduces a new mathematical framework for classification and recognition problems in computer vision, especially face recognition. The basic idea is to cast recognition as a sparse representation problem, utilizing new mathematical tools from compressed sensing and L1 minimization. This leads to highly robust, scalable algorithms for face recognition based on linear or conv
Accurate, Fast, Developer- Friendly ALPR Automatic License Plate Recognition software that works in all environments, optimized for your location.
Baidu’s Artificial-Intelligence Supercomputer Beats Google at Image Recognition
Baidu’s Artificial-Intelligence Supercomputer Beats Google at Image Recognition A supercomputer specialized for the machine-learning technique known as deep learning could help software understand us better. Update: On June 1, 2015, Baidu amended its technical paper on its system to admit that it had broken rules governing the ImageNet Challenge that the company had used to claim it had beaten oth
Identify and tag family & friends Built in face recognition Organize your photos into albums Facebook and Flickr integration View photos from your mobile device Easy-to-use drag & drop interface Built-in face recognition. Stop wasting hours individually tagging your photos! Let Fotobounce do it!
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Netlab: Algorithms for Pattern Recognition
Neural Network for Recognition of Handwritten Digits
FaceNet: A Unified Embedding for Face Recognition and Clustering
誰でもできる機械学習 Watson Visual Recognition（画像認識）の使い方
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Named-entity recognition - Wikipedia
Loop Recognition in C++/Java/Go/Scala
CS231n Convolutional Neural Networks for Visual Recognition
CS231n Convolutional Neural Networks for Visual Recognition
Building an image recognition React app using ONNX.js - Fritz ai
KeyLemon – Face Recognition Technology
Pattern Recognition and Machine Learning 2. Probability Distribution 2.1 Binary Variables
Chapter 12. Barcode recognition
The Face Recognition Algorithm That Finally Outperforms Humans
High Quality Face Recognition with Deep Metric Learning
Traffic Sign Recognition with TensorFlow
http://finest.se/taimefortai/2018/02/08/facial-recognition-dating-site/
Face Recognition via Sparse Representation
License Plate Recognition API - High accuracy ANPR
Fotobounce - Photo organizing with face recognition