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Open AccessJournal ArticleDOI

The Automatic Speech Recognition System for Conversational Sound

Toshiyuki Sakai, +1 more
- 01 Dec 1963 - 
- Vol. 12, Iss: 6, pp 835-846
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TLDR
This paper describes the method and the system investigated to solve the problem encountered in the automatic recognition of speech sound, a monosyllable recognition system in which the phoneme is used as the basic recognition unit.
Abstract
This paper describes the method and the system investigated to solve the problem encountered in the automatic recognition of speech sound From research in the automatic analyzer of speech sound, a monosyllable recognition system was constructed in which the phoneme is used as the basic recognition unit Recently this system has been developed to accept the conversational speech sound with unlimited vocabulary The mechanical recognition of conversational speech sound requires two basic operations One is the segmentation of the continuous speech sound into several discrete intervals (or segments), each of which may be thought to correspond to a phoneme, and the other is the pattern recognition of such segments For segmentation, by defining two criteria, ``stability'' and ``distance,'' the properties of the time pattern obtained by the analysis of input speech sound may be examined The principle of the recognition is based on the mechanism of the articulation in our speech organ Corresponding to this, the machine has the functions called phoneme classification, vowel analysis and consonant analysis A conversational speech recognition system with the phonetic contextual approach is also applied to the vowel recognition where the time pattern of input speech is matched with the stored standard patterns in which the phonetic contextual effects are taken into consideration The time pattern which has great variety may be effectively expressed by the new representation of ``sequential pattern'' and ``weighting pattern''

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State of the art in pattern recognition

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Fuzzy sets and decisionmaking approaches in vowel and speaker recognition

TL;DR: 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 speech sounds with a recognition score of 82 percent for vowels and 97 percent for the speaker.
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Random-Pulse Machines

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Syllabic (~2-5 Hz) and fluctuation (~1-10 Hz) ranges in speech and auditory processing

TL;DR: Evidence is found that the temporal modulation transfer function (TMTF) of human auditory perception is not simply low-pass in nature, but rather exhibits a peak in sensitivity in the syllabic range (∼2-5 Hz).
References
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Journal ArticleDOI

New Instruments and Methods for Speech Analysis

TL;DR: In this paper, three kinds of devices are described which have been developed by the authors to analyze speech sounds, which include devices to analyze automatically the zero-crossing intervals, display the zero crossing waves in three-dimensional form, and make a visible pattern of the zero crossing waves.