Data Mining Practical Machine Learning Tools and Techniques

From the Publisher:
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

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The Ministry of Education

Although there has been a lot of interest in recognizing and understanding air traffic control (ATC) speech, none of the published works have obtained detailed field data results. We have developed a system able to identify the language spoken and recognize and understand sentences in both Spanish and English. We also present field results for several in-tower controller positions. To the best of our knowledge, this is the first time that field ATC speech (not simulated) is captured, processed, and analyzed. The use of stochastic grammars allows variations in the standard phraseology that appear in field data. The robust understanding algorithm developed has 95% concept accuracy from ATC text input. It also allows changes in the presentation order of the concepts and the correction of errors created by the speech recognition engine improving it by 17% and 25%, respectively, absolute in the percentage of fully correctly understood sentences for English and Spanish in relation to the percentages of fully correctly recognized sentences. The analysis of errors due to the spontaneity of the speech and its comparison to read speech is also carried out. A 96% word accuracy for read speech is reduced to 86% word accuracy for field ATC data for Spanish for the "clearances" task confirming that field data is needed to estimate the performance of a system. A literature review and a critical discussion on the possibilities of speech recognition and understanding technology applied to ATC speech are also given.

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Automatic Understanding of ATC Speech: Study of Prospectives and Field Experiments for Several Controller Positions

Combining pulse-based features for rejecting far-field speech in a HMM-based Voice Activity Detector

This presents a robust voice activity detector (VAD) based on Hidden Markov Models (HMM) in stationary and non-stationary noise environments: inside motor vehicles (like cars or planes) or inside buildings close to high traffic places (like in a control tower for air traffic control (ATC)). In these environments, there is a high stationary noise level caused by vehicle motors and additionally, there could be people speaking at certain distance from the main speaker producing non-stationary noise. The VAD presented herein is characterized by a new front-end and a noise level adaptation process that increases significantly the VAD robustness for different signal to noise ratios (SNRs). The feature vector used by the VAD includes the most relevant Mel Frequency Cepstral Coefficients (MFCC), normalized log energy, and delta log energy. The proposed VAD has been evaluated and compared to other well-known VADs using three databases containing different noise conditions: speech in clean environments (SNRs >; 20 dB), speech recorded in stationary noise environments (inside or close to motor vehicles), and finally, speech in non-stationary environments (including noise from bars, television, and far-field speakers). In the three cases, the detection error obtained with the proposed VAD is the lowest for all SNRs compared to Acero's VAD (reference of this work [4]) and other well-known VADs like AMR, AURORA, or G729 annex b.

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Robust speech detection for noisy environments

Bayesian Networks, BNs, are suitable for mixed-initiative dialog modeling allowing a more flexible and natural spoken interaction. This solution can be applied to identify the intention of the user considering the concepts extracted from the last utterance and the dialog context. Subsequently, in order to make a correct decision regarding how the dialog should continue, unnecessary, missing, wrong, optional and required concepts have to be detected according to the inferred goals. This information is useful to properly drive the dialog prompting for missing concepts, clarifying for wrong concepts, ignoring unnecessary concepts and retrieving those required and optional. This paper presents a novel BNs approach where a single BN is obtained from N goal-specific BNs through a fusion process. The new fusion BN enables a single concept analysis which is more consistent with the whole dialog context.

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A Bayesian NETWORKS approach for dialog modeling: The fusion BN

In this article, we propose and evaluate a deep learning architecture based on convolutional neural networks for human stress detection using wearable sensors. This architecture has a first part that includes three convolutional layers for learning features from several biosignals. The second part is composed of three fully connected layers for stress detection. Additionally, we analyze several biosignal processing techniques to be applied before defining the inputs to the deep learning architecture: Fourier transform, cube root and constant Q transform. This analysis was performed on a public dataset, wearable stress and affect detection dataset (recorded by Robert Bosch GmbH Corporate Research, Germany), using a leave-one-subject-out cross-validation. We evaluated three different classification tasks including different emotional states. The accuracy increased from 93.1% to 96.6% when classifying stress vs. nonstress states, from 80.3% to 85.1% for differencing baseline versus stress versus amusement, and from 77.1% to 82.1% when discriminating five classes: baseline, stress, amusement, meditation, and recovery.

Rubén San-Segundo

Papers

Automatic Understanding of ATC Speech: Study of Prospectives and Field Experiments for Several Controller Positions

Combining pulse-based features for rejecting far-field speech in a HMM-based Voice Activity Detector

Robust speech detection for noisy environments

A Bayesian NETWORKS approach for dialog modeling: The fusion BN

Human Stress Detection With Wearable Sensors Using Convolutional Neural Networks