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.

/pdf/speech-and-language-processing-an-introduction-to-natural-atzaecqfem.pdf

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

4 CLINICAL PARTICULARS 4.1 Therapeutic Indications Trimetazidine is indicated in adults as add-on therapy for the symptomatic treatment of patients with stable angina pectoris who are inadequately controlled by or intolerant to first-line antianginal therapies. 4.2 Posology and method of administration Oral administration. Posology The dose is one tablet of 35mg of trimetazidine twice daily during meals. Special populations Renal impairment In patients with moderate renal impairment (creatinine clearance [30-60] ml/min) (see sections 4.4 and 5.2), the recommended dose is 1 tablet of 35mg in the morning during breakfast. Elderly Elderly patients may have increased trimetazidine exposure due to age-related decrease in renal function (see section 5.2). In patients with moderate renal impairment (creatinine clearance [30-60] ml/min), the recommended dose is 1 tablet of 35mg in the morning during breakfast. Dose titration in elderly patients should be exercised with caution (see section 4.4). Health Products Regulatory Authority

Summary of product characteristics

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

http://www.ffri.hr/~ibrdar/komunikacija/seminari/Ververidis,%202006%20-%20Emotional%20speech%20recognition.pdf

Emotional speech recognition: Resources, features, and methods

Speaker diarization is the task of determining “who spoke when?” in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper, we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research.

/pdf/speaker-diarization-a-review-of-recent-research-5cp6zhupyq.pdf

Speaker Diarization: A Review of Recent Research

This paper provides improved confidence assessment for detection of word-level speech recognition errors and out-of-domain user requests using language model features. We consider a combined measure of confidence that utilizes the language model back-off sequence, language model score, and phonetic length of recognized words as indicators of speech recognition confidence. The paper investigates the ability of each feature to detect speech recognition errors and out-of-domain utterances as well as two methods for combining the features contextually: a multi-layer perceptron and a statistical decision tree. We illustrate the effectiveness of the algorithm by considering utterances from the ATIS airline information task as either in-domain and out-of-domain for the DARPA Communicator task. Using this hand-labeled data, it is shown that 27.9% of incorrectly recognized words and 36.4% of out-of-domain phrases are detected at a 2.5% false alarm rate.

/pdf/confidence-measures-for-dialogue-management-in-the-cu-4ynizrwnr8.pdf

Confidence measures for dialogue management in the CU Communicator system

A number of recent studies have focused on the conceptualized expression of emotions as a three-dimensional space. This paper proposes a new approach to emotion recognition, making use of two of the emotional dimensions and their relationship with different kinds of features. The main idea consists in associating prosodic features, derived from pitch, loudness, and duration, with the activation or arousal dimension, and quality features, i.e. phonation type, articulation manner, voice timbre, with the evaluation and pleasure dimension, in a way that different classification methods can be applied for each specific case. Most important results are achieved for the speaker-independent case and three classes, with a recognition rate close to 80%.

/pdf/emotional-space-improves-emotion-recognition-3hvk7rlaiw.pdf

Emotional space improves emotion recognition

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks

http://www-gth.die.upm.es/research/documentation/AI-106Spe-08.pdf

Speech to sign language translation system for Spanish

Human-machine interaction in meetings requires the localization and identification of the speakers interacting with the system as well as the recognition of the words spoken. A seminal step toward this goal is the field of rich transcription research, which includes speaker diarization together with the annotation of sentence boundaries and the elimination of speaker disfluencies. The sub-area of speaker diarization attempts to identify the number of participants in a meeting and create a list of speech time intervals for each such participant. In this paper, we analyze the correlation between signals coming from multiple microphones and propose an improved method for carrying out speaker diarization for meetings with multiple distant microphones. The proposed algorithm makes use of acoustic information and information from the delays between signals coming from the different sources. Using this procedure, we were able to achieve state-of-the-art performance in the NIST spring 2006 rich transcription evaluation, improving the Diarization Error Rate (DER) by 15% to 20% relative to previous systems.

/pdf/speaker-diarization-for-multiple-distant-microphone-meetings-23cv4v50ls.pdf

José Manuel Pardo

Papers

Confidence measures for dialogue management in the CU Communicator system

Emotional space improves emotion recognition

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks

Speech to sign language translation system for Spanish

Speaker Diarization For Multiple-Distant-Microphone Meetings Using Several Sources of Information