This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

A comprehensive review for industrial applicability of artificial neural networks

A system for synthesizing a speech signal from strings of words, which are themselves strings of characters, includes a memory in which predetermined syntax tags are stored in association with entered words and phonetic transcriptions are stored in association with the syntax tags. A parser accesses the memory and groups the syntax tags of the entered words into phrases according to a first set of predetermined grammatical rules relating the syntax tags to one another. The parser also verifies the conformance of sequences of the phrases to a second set of predetermined grammatical rules relating the phrases to one another. The system retrieves the phonetic transcriptions associated with the syntax tags that were grouped into phrases conforming to the second set of rules, and also translates predetermined strings of characters into words. The system generates strings of phonetic transcriptions and prosody markers corresponding to respective strings of the words, and adds markers for rhythm and stress to the strings, which are then converted into data arrays having prosody information on a diphone-by-diphone basis. Predetermined diphone waveforms are retrieved from memory that correspond to the entered words, and these retrieved waveforms are adjusted based on the prosody information in the arrays. The adjusted diphone waveforms, which may also be adjusted for coarticulation, are then concatenated to form the speech signal. Methods in a digital computer are also disclosed.

Method and apparatus for speech synthesis based on prosodic analysis

This paper reviews statistical, adaptive, and heuristic techniques used in laboratory investigations of pattern recognition problems. The discussion includes correlation methods, discriminant analysis, maximum likelihood decisions minimax techniques, perceptron-like algorithms, feature extraction, preprocessing, clustering and nonsupervised learning. Two-dimensional distributions are used to illustrate the properties of the various procedures. Several experimental projects, representative of prospective applications, are also described.

State of the art in pattern recognition

Designing a machine that mimics human behavior, particularly the capability of speaking naturally and responding properly to spoken language, has intrigued engineers and scientists for centuries Since the 1930s, when Homer Dudley of Bell Laboratories proposed a system model for speech analysis and synthesis [1, 2], the problem of automatic speech recognition has been approached progressively, from a simple machine that responds to a small set of sounds to a sophisticated system that responds to fluently spoken natural language and takes into account the varying statistics of the language in which the speech is produced Based on major advances in statistical modeling of speech in the 1980s, automatic speech recognition systems today find widespread application in tasks that require a human-machine interface, such as automatic call processing in the telephone network and query-based information systems that do things like provide updated travel information, stock price quotations, weather reports, etc In this article, we review some major highlights in the research and development of automatic speech recognition during the last few decades so as to provide a technological perspective and an appreciation of the fundamental progress that has been made in this important area of information and communication technology

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Automatic Speech Recognition - A Brief History of the Technology Development

Some applications based on the theory of fuzzy sets in problems of computer recognition of vowels and identifying the person from his spoken words using only the first three formants (F 1, F2, and F3) of the unknown utterance are presented. Two decision algorithmic methods using weighted-distance functions and property sets are developed and implemented with the optimum size of the training set on a large number of Telugu (an important Indian language) speech sounds with a recognition score of 82 percent for vowels and 97 percent for the speaker.

Fuzzy sets and decisionmaking approaches in vowel and speaker recognition

http://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/52601/1/soa006_051.pdf

Speech Analysis-Synthesis and Recognition System.

https://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/52616/1/soa004_090.pdf

Toshiyuki Sakai

Papers

Speech Analysis-Synthesis and Recognition System.

A Procedure for Formant Domain Extraction