Linear Regression Analysis

Systems and processes are disclosed for handling a multi-part voice command for a virtual assistant. Speech input can be received from a user that includes multiple actionable commands within a single utterance. A text string can be generated from the speech input using a speech transcription process. The text string can be parsed into multiple candidate substrings based on domain keywords, imperative verbs, predetermined substring lengths, or the like. For each candidate substring, a probability can be determined indicating whether the candidate substring corresponds to an actionable command. Such probabilities can be determined based on semantic coherence, similarity to user request templates, querying services to determine manageability, or the like. If the probabilities exceed a threshold, the user intent of each substring can be determined, processes associated with the user intents can be executed, and an acknowledgment can be provided to the user.

Multi-command single utterance input method

A mobile electronic device has a touch sensitive screen and an accelerometer. A translator is to translate a word or phrase that is in a first human language and that is entered via a first virtual keyboard displayed on the touch sensitive screen, into a second human language. A translator is to cause the touch sensitive screen to display the translated word or phrase and a second virtual keyboard having characters in the second human language, in response to the accelerometer detecting a change in the physical orientation of the device or movement of the device. Other embodiments are also described and claimed.

Mobile device having human language translation capability with positional feedback

This is directed to processing voice inputs received by an electronic device. In particular, this is directed to receiving a voice input and identifying the user providing the voice input. The voice input can be processed using a subset of words from a library used to identify the words or phrases of the voice input. The particular subset can be selected such that voice inputs provided by the user are more likely to include words from the subset. The subset of the library can be selected using any suitable approach, including for example based on the user's interests and words that relate to those interests. For example, the subset can include one or more words related to media items selected by the user for storage on the electronic device, names of the user's contacts, applications or processes used by the user, or any other words relating to the user's interactions with the device.

User profiling for voice input processing

This relates to systems and processes for using a virtual assistant to control electronic devices. In one example process, a user can speak an input in natural language form to a user device to control one or more electronic devices. The user device can transmit the user speech to a server to be converted into a textual representation. The server can identify the one or more electronic devices and appropriate commands to be performed by the one or more electronic devices based on the textual representation. The identified one or more devices and commands to be performed can be transmitted back to the user device, which can forward the commands to the appropriate one or more electronic devices for execution. In response to receiving the commands, the one or more electronic devices can perform the commands and transmit their current states to the user device.

Intelligent assistant for home automation

Chat-bots are computer programs coded to have a textual or verbal conversation which is logical or intelligent. Chat-bots are designed to make humans believe that they are talking to a human; but instead they are in fact talking to a machine. Taking advantage of this transparency property of chat-bot, an artificial character and personality can be given to a chat-bot which acts like a person of a specific profession. This paper describes an approach to the idea of implementing web-based artificially intelligent chat-bot as a personal assistant of the user, which stimulates setting and initiating meetings of user with his clients. The exchange of information happens through email conversations whereas its evaluation happens through natural language procession and natural language generation and AIML files. General Terms AIML, Artificial Intelligence, Pattern Matching.

/pdf/donna-interactive-chat-bot-acting-as-a-personal-assistant-3k7zgnfcha.pdf

Donna Interactive Chat-bot acting as a Personal Assistant

Curve fitting gives the user a mathematical function that best fits to a series of data points while considering the constraints of the data. This paper presents an algorithm to determine the equation of a hand-drawn curve using polynomial regression. The hand-drawn curve may be digitally drawn, or manually drawn on paper and scanned. Polynomial regression is used to estimate the order of the equation that fits the curve and determine the coefficients of the equation. General Terms Regression, Machine Learning, Curve Fitting, Hand-Drawn, Image Processing.

/pdf/finding-best-fit-for-hand-drawn-curves-using-polynomial-63spbloeme.pdf

Finding Best Fit for Hand-Drawn Curves using Polynomial Regression

Convolutional neural networks have recently become extremely popular in various deep learning applications. One such application is style transfer for images. Following this trend, this paper explores how this technique can be applied to audio data. The technique discussed involves combining the content features of one audio sample with the style features of another audio sample. The results produced show how a Convolutional Neural Network can be used to extract features from audio signals. The paper also discusses the various modifications made in the algorithm used for image style transfer in order to apply it to audio signals. General Terms Machine Learning, Neural Networks.

/pdf/style-transfer-for-audio-using-convolutional-neural-networks-1hzbyze0j0.pdf

Style Transfer for Audio using Convolutional Neural Networks

Humor is an integral part of our day-to-day communication making it interesting and plausible. The growing demand in robotics and the constant urge for machines to pass the Turing test to eliminate the thin line difference between human and machine behavior make it essential for machines to be able to use humor in communication. Moreover, Learning is a continuous process and very important at every stage of life. However sometimes merely reading from a book induces lassitude and lack of concentration may hamper strong foundation building. Children suffering from Autism Spectrum Disorder (ASD) suffer from slow learning and grasping issues. English being a funny language, a particular word has multiple meanings, making it difficult for children with ASD to cognize it. Solving riddles induces fast learning and sharpness in children including those affected by ASD. The existing systems however, are too far from being used in any practical application. This paper proposes a system that uses core ideas of JAPE to create puns for entertainment and vocabulary building purpose for children. General Terms Homophone: Two or more words having the same pronunciation but different meanings, origins, or spelling (e.g. new and knew) [4]. Homonym: Two or more words having the same spelling or pronunciation but different meanings and origins (e.g. pole. and pole) [5]. Rhyming words: Words that have the same ending sounds. E.g. are cat, hat, bat, mat, fat and rat [6]. Punning words: A form of word play that suggests two or more meanings, by exploiting multiple meanings of words, or of similar-sounding words, for an intended humorous or rhetorical effect [7]. Pun generator: A system that uses punning words to generate riddles/jokes with an intention of making it humorous.

/pdf/computational-creativity-automated-pun-generation-4ifcqiteuv.pdf

Computational Creativity: Automated Pun Generation

The internet is a large storehouse of information. To deliver information efficiently, the audience can be segregated by demographic attributes which can be individually targeted. Companies may be able to obtain or collect information about users' browsing history. Proposed in this paper is a system using TF-IDF and a Neural network, to estimate a user's age, gender and interests by analyzing their browser history. General Terms Data mining, neural network.

/pdf/a-system-to-determine-demographic-attributes-using-local-3frgztvmkd.pdf

Swati Mali

Papers

Donna Interactive Chat-bot acting as a Personal Assistant

Finding Best Fit for Hand-Drawn Curves using Polynomial Regression

Style Transfer for Audio using Convolutional Neural Networks

Computational Creativity: Automated Pun Generation

A System to Determine Demographic Attributes using Local Browsing History