Miscellaneous

Chief Research Officer Rick Rashid demonstrates a speech recognition breakthrough via machine translation that converts his spoken English words into computer-generated Chinese language. The breakthrough is patterned after deep neural networks and significantly reduces errors in spoken as well as written translation.

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Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects).

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This is my first test of the MOVI voice recognition (and speech) shield for the Arduino. It will be used on "Little Friend" on hackaday.io

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Lip Reading Sentences in the Wild - Joon Son Chung, Andrew Senior, Oriol Vinyals, Andrew Zisserman

Abstract:

The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem – unconstrained natural language sentences, and in the wild videos.

Our key contributions are: (1) a ‘Watch, Listen, Attend and Spell’ (WLAS) network that learns to transcribe videos of mouth motion to characters; (2) a curriculum learning strategy to accelerate training and to reduce overfitting; (3) a ‘Lip Reading Sentences’ (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television.

The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that visual information helps to improve speech recognition performance even when the audio is available.

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In this video, we'll make a super simple speech recognizer in 20 lines of Python using the Tensorflow machine learning library. I go over the history of speech recognition research, then explain (and rap about) how we can build our own speech recognition system using the power of deep learning.

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The artificial intelligence can solve the "cocktail party problem"; with 90 percent accuracy and will soon be installed in public places.

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