Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC, LPCC, PLP, RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions

Këpuska, Veton; Elharati, Hussien A.

doi:10.4236/jcc.2015.36001

Cited by 54 publications

(21 citation statements)

References 5 publications

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“…If we want to understand in better way, we need to understand first, for which types analytics can be applied let us say first and foremost text analysis [17] which says to extract information from unstructured data using IE [18], text summarization [19], QA system which is based on the speech analysis [20] which chooses two approach transcriptbased and phonetic based approach. Scientists are still conducting experiments about how to derive useful knowledge through video streams, the very popular use of video analytics in the protection security and monitoring systems [21].…”

Section: Big Data Analysismentioning

confidence: 99%

Research Perspective on Security Based Algorithm in Big Data Concepts

Rai,

Sharma

2020

IJEAT

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Providing a robust security for large data is one in all the first concern for most of the researchers. This paper makes an attempt to uncover all the protection solutions associated with unstructured, structured and semi structured data. also, the main aim of this paper is to cover the information related with the several encryption algorithms used to provide confidentiality, integrity, privacy and data silos. Different algorithmic program and tools play an efficient role in playacting significant analysis on huge volume, variety of big data.

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Section: Big Data Analysismentioning

confidence: 99%

Research Perspective on Security Based Algorithm in Big Data Concepts

Rai,

Sharma

2020

IJEAT

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“…They trained the system for one time and continuous testing phases [9]. In 2015, Veton Z. Këpuska and Hussien A. Elhara investigates the performance conventional and new hybrid feature extraction techniques of MFCC, Linear Predictive coding coefficient (LPCC),perceptual linear production (PLP), and RASTA-PLP with HMM as classifier [10]. In 2016, K. shen et.…”

Section: Literature Surveymentioning

confidence: 99%

Keyword Detection using Auto Associative Neural Network with Reference to Assamese Language

Kalita¹,

Borbora²

2019

IJRTE

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Speech and speaker recognition has yet not achieved the state-of-the-art position. Keyword detection in audio clips is gaining importance as it contributes to the audio recognition and detection systems. In this area, very few works have been carried out. In this paper, we present our experiment on keyword detection within recorded news clips. It is based on Assamese language spoken by Assamese native speakers. For this experiment, the audio clips are collected from local TV news debates, whereas the keywords are recorded by random speakers. The keywords are selected for recording considering the fact that they appear somewhere within the audio clips for a finite number of times. Mel Frequency Cepstral Coefficient (MFCC) is considered as feature and Auto Associative Neural Network (AANN) is considered as the classifier tool. With this detection model an average accuracy of 87% is achieved.

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“…Speech recognition system involved in several applications such as: call routing, automatic transcriptions, information searching, data entry, Speech to Text conversion, Text to Speech conversion etc. [3].…”

Section: Introductionmentioning

confidence: 99%

Arabic Speech Recognition System Based on MFCC and HMMs

Elharati¹,

Alshaari²,

Këpuska³

2020

JCC

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Speech recognition allows the machine to turn the speech signal into text through identification and understanding process. Extract the features, predict the maximum likelihood, and generate the models of the input speech signal are considered the most important steps to configure the Automatic Speech Recognition System (ASR). In this paper, an automatic Arabic speech recognition system was established using MATLAB and 24 Arabic words Consonant-Vowel Consonant-Vowel Consonant-Vowel (CVCVCV) was recorded from 19 Arabic native speakers, each speaker uttering the same word 3 times (total 1368 words). In order to test the system, 39-features were extracted by partitioning the speech signal into frames ~ 0.25 sec shifted by 0.10 sec. in back-end, the statistical models were generated by separated the features into number of states between 4 to 10, each state has 8-gaussian distributions. The data has 48 k sample rate and 32-bit depth and saved separately in a wave file format. The system was trained in phonetically rich and balanced Arabic speech words list (10 speakers * 3 times * 24 words, total 720 words) and tested using another word list (24 words * 9 speakers * 3 times *, total 648 words). Using different speakers similar words, the system obtained a very good word recognition accuracy results of 92.92% and a Word Error Rate (WER) of 7.08%.

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Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC, LPCC, PLP, RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions

Cited by 54 publications

References 5 publications

Research Perspective on Security Based Algorithm in Big Data Concepts

Research Perspective on Security Based Algorithm in Big Data Concepts

Keyword Detection using Auto Associative Neural Network with Reference to Assamese Language

Arabic Speech Recognition System Based on MFCC and HMMs

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