Using multiple HMM recognizers and the maximum accuracy method to improve voice-controlled robots

Phoophuangpairoj, Rong

doi:10.1109/ispacs.2011.6146111

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…In direct proportion to the magnitude of manoeuvres, the psychological state of the cruise control person, the data provided to the command reference-bank during the software training phase might show discrepancies [19]. Except in the case of fear, stress-related anger, mood changes such as sadness, have been observed to increase the success of the voice recognition system [20].…”

Section: Discussionmentioning

confidence: 99%

Navigation Security Module with Real-Time Voice Command Recognition System

Yağımlı

Tezer

2017

Polish Maritime Research

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Section: Discussionmentioning

confidence: 99%

Navigation Security Module with Real-Time Voice Command Recognition System

Yağımlı

Tezer

2017

Polish Maritime Research

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“…These acoustic models are found in many speech recognition systems [1][2] and the HMMs of syllable units are efficiently used in speech recognition systems [3]. Mel Frequency Cepstral Coefficients (MFCCs) are acoustic features that have been widely used to recognize speech signals [1][2][3][4][5]. Furthermore, MFCC-based acoustic features and pitch contours extracted from human voices are used to determine tones in languages such as Thai, Cantonese and Vietnamese [6][7][8].…”

Section: Applied Mechanics and Materialsmentioning

confidence: 99%

Computerized Unripe and Ripe Durian Striking Sound Recognition Using Syllable-Based HMMs

Phoophuangpairoj

2013

AMM

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Buying expensive agricultural produce and fruit such as durians that are unripe can result in a bad experience for a consumer and a loss in profit for a retailer. Therefore, the study of durian striking sounds to create an automatic method of recognizing the ripeness of durians without cutting or damaging them is interesting because it could benefit shoppers and the fruit industry. To solve the problem, the following method of recognizing unripe and ripe striking signals is proposed. First, in the recognition process, the spectral features of the signals are extracted. Then, acoustic models of durian striking sounds are created using syllable-based Hidden Markov Models (HMM). Finally, sequences of syllable-based unripe and ripe durians and defined possible durian ripeness results are applied to recognize the ripeness. Average ripeness recognition rates of more than 90.0% were achieved when using any number of strikes from one to five. When the number of strikes was limited to four and five, higher recognition rates of 95.0% and 92.0% were achieved for the untrained and unknown test sets, respectively. An average total recognition time of less than 80 milliseconds was taken to recognize the unripe and ripe durian striking sounds. The experimental results indicate that the proposed method is a time-efficient, accurate and effective way of recognizing durian ripeness.

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“…More than 80% success has been achieved in voice recognition on the letters "a", "e" and "i" [10]. Separate tests were conducted on male and female users [11] and a voice recognition system was used [12].…”

Section: Introductionmentioning

confidence: 99%

A Voice Recognition Based Game Design for More Accurate Pronunciation of English

Avuclu,

Koklu

2022

imiens

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The development of technology has made it considerably easier for people to meet a number of needs. Without technology, it is no longer possible to run certain applications. Nowadays, in many countries, the process of speaking English poses some problems in terms of different situations, such as allocating time for people. In this study, a game-based application was developed to help a person anywhere in the world pronounce English better. The application was implemented in C # programming language. The Speech.dll library was used to introduce voice commands to the system and to perform other necessary operations. Voice commands can be sent by the user via the wireless headset from anywhere in the shooting area. There is no need to wait at the computer while using the application because the developed application gives voice feedback to the user that it is right or wrong after the voice recognition process. In this application letter, word or sentence exercises can be done. The program aims to improve the level of pronunciation of people who want to improve their English speaking skills.

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Using multiple HMM recognizers and the maximum accuracy method to improve voice-controlled robots

Cited by 14 publications

References 10 publications

Navigation Security Module with Real-Time Voice Command Recognition System

Navigation Security Module with Real-Time Voice Command Recognition System

Computerized Unripe and Ripe Durian Striking Sound Recognition Using Syllable-Based HMMs

A Voice Recognition Based Game Design for More Accurate Pronunciation of English

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