A detailed survey on large vocabulary continuous speech recognition techniques

Vanajakshi, Puttaswamy Gowda; Mathivanan, M.

doi:10.1109/iccci.2017.8117755

Cited by 3 publications

(6 citation statements)

References 3 publications

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“…Speech recognition systems are classified based on the speech recognition approaches, speech utterance, speaker model, feature extraction, and terminology [4,35].…”

Section: Types Of Speech Recognitionmentioning

confidence: 99%

“…Speech recognition systems are basically divided into four categories based on speech utterance [35]. Isolated word recognition requires remaining silent on both sides of the sample windows.…”

Section: Speech Utterancementioning

confidence: 99%

“…Such ASR systems, which are able to recognize voices spontaneously, recognize various natural sounds (hmm, uh, silence, etc.) [35].…”

Section: Speech Utterancementioning

confidence: 99%

“…The development of such systems is cumbersome, requires difficult problems to be solved, and may yield results with less precision (accuracy). However, these systems are more flexible and can cope with different situations [35].…”

Section: Speaker Modelmentioning

confidence: 99%

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A detailed survey of Turkish automatic speech recognition

2020

Turk J Elec Eng & Comp Sci

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Significant improvements have been made in automatic speech recognition (ASR) systems in terms of both the general technology and the software used. Despite these advancements, however, there is still an important difference between the recognition performance of humans and machines. This work focuses on the studies conducted in the field of Turkish speech recognition, the progress made in such studies in recent years, the language-specific constraints, the performance results achieved in the applications developed to date, and the development of a general scheme for researchers wishing to develop an ASR system for the Turkish language. A comprehensive study on the Turkish language, including all literature and speech recognition systems, was prepared. There has been significant progress in the languages of developed countries in terms of ASR systems, but it was observed in this work that the opposite is true for the Turkish language. The lack of labor force and material resources remains the biggest obstacle to developing better systems. As a result, concrete studies are needed to achieve an ASR system with performance equivalent to the human level for the Turkish language.

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“…Speech recognition systems are classified based on the speech recognition approaches, speech utterance, speaker model, feature extraction, and terminology [4,35].…”

Section: Types Of Speech Recognitionmentioning

confidence: 99%

Section: Speech Utterancementioning

confidence: 99%

“…Such ASR systems, which are able to recognize voices spontaneously, recognize various natural sounds (hmm, uh, silence, etc.) [35].…”

Section: Speech Utterancementioning

confidence: 99%

Section: Speaker Modelmentioning

confidence: 99%

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A detailed survey of Turkish automatic speech recognition

2020

Turk J Elec Eng & Comp Sci

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“…Efficiently tested the performance of MFCC, LPC and PLP feature extraction technique in voice [3]. Modular Neural Network (MNN) for speech recognition is presented with speaker dependent single word recognition and classification [4].The Automatic speech recognition has made great strides with the development of digital signal processing hardware and software specifically in case of speaker independent speech recognition so considering the speech feature extraction such as PLP [5].The helps in choosing the technique along with their relative merits demerits. A comparative study of different technique is done [6].…”

Section: Introductionmentioning

confidence: 99%

A Review on Speech Feature Techniques and Classification Techniques

Bodke¹,

M.P.Satone²

2018

IJTSRD

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Speech Processing method is one of the important method used in application area of digital and analog signal processing. It is used in real world speech processing of human language such as human computer interface system for home, industry and medical field. It is the most common means of the communication because the information contains the fundamental role in conversation. From the speech or conversation, it converts an acoustic signal that is captured by a microphone or a telephone, to a set of words. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. Speech recognition is the process of automatically recognizing the spoken words of person based on information content in speech signal. The introduces a brief detail study on Automatic Speech Recognition and discusses the various classification techniques that have been accomplished in this wide area of speech processing. The objective of this paper is to study some of the well known methods that are widely used in several stages of speech recognition system.

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Framework for Knowledge Discovery in Educational Video Repositories

Gomes

Dias

Soares

et al. 2019

cai

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The ease of creating digital content coupled with technological advancements allows institutions and organizations to further embrace distance learning. Teaching materials also receive attention, because it is difficult for the student to obtain adequate didactic material, being necessary a high effort and knowledge about the material and the repository. This work presents a framework that enables the automatic metadata generation for materials available in educational video repositories. Each module of the framework works autonomously and can be used in isolation, complemented by another technique or replaced by a more appropriate approach to the field of use, such as repositories with other types of media or other content.

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A detailed survey on large vocabulary continuous speech recognition techniques

Cited by 3 publications

References 3 publications

A detailed survey of Turkish automatic speech recognition

A detailed survey of Turkish automatic speech recognition

A Review on Speech Feature Techniques and Classification Techniques

Framework for Knowledge Discovery in Educational Video Repositories

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