{"id":2341,"date":"2019-01-31T16:24:45","date_gmt":"2019-01-31T16:24:45","guid":{"rendered":"http:\/\/www.gyanvihar.org\/journals\/?p=2341"},"modified":"2019-01-31T16:24:45","modified_gmt":"2019-01-31T16:24:45","slug":"speech-signal-processing-a-technical-review","status":"publish","type":"post","link":"https:\/\/www.gyanvihar.org\/journals\/speech-signal-processing-a-technical-review\/","title":{"rendered":"SPEECH SIGNAL PROCESSING: A TECHNICAL REVIEW"},"content":{"rendered":"

pp 26-28<\/strong><\/p>\n

Subhash Chand Samota1<\/sup>, Gaurav Kumar Sharma2<\/sup><\/strong><\/p>\n

M.Tech scholar, SGVU, Jaipur<\/p>\n

samota8@gmail.com<\/p>\n

Associate Professor, SGVU, Jaipur<\/p>\n

gauravkr.sharma@mygyanvihar.com<\/p>\n

\n

Abstract:<\/strong> Here in this paper we are reviewing the last four years work in the field of speech signal processing. We have reviewed the four latest papers of speech signal processing and all these papers published from 2017 to 2018. These papers are published in IEEE journals and conference proceeding. So the complete review process is based on the recent research in speech signal processing from last three years.<\/p>\n

Keywords: <\/strong>Speech Signal Processing, Fast Fourier Transform, Framing, Discrete Cosine Transform<\/p>\n

 <\/p>\n

I. INTRODUCTION<\/strong><\/p>\n

Speech signal processing is an active research area in the field of digital signal processing. Speech signal processing was come into the picture in 70\u2019s. Some of the important aspects of digital speech processing are high quality coding (perceptual coding) of speech and audio, speech recognition, enhancement and modification of speech and audio, text-to-speech synthesis, speech-to-text synthesis etc.\u00a0<\/em><\/strong>Here in this paper we are reviewing the last four years work in the field of speech signal processing. We have reviewed the four papers of speech signal processing and all these papers published from 2017 to 2018. Three papers are published in 2018and one papers was published in 2017. These papers are published in IEEE journals and conference proceeding. So the complete review process is based on the recent research in speech signal processing from last three years.<\/p>\n

II. REVIEW OF PAPERS<\/strong><\/p>\n

Review.1:<\/strong><\/p>\n

Micha\u0142 Raczy\u0144ski et-al, 2018, describe an algorithm for recognize the sound from previously created constant words. They use the MATLAB to perform this task. First they pass the signal through the filter. They record the signal by matlab music card for 1sec. then convert the analog signal into digital signal with sampling frequency 8 khz. \u00a0Obtained signal is fitted to constant length of frame equal 6000 samples, using resembling method. They use the PLA technique to recognize the world. Complete set of operational diagram is shown in Fig.1<\/p>\n

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The performance criterion was mean square error. The testing result of the above experiment was 80% accurate.<\/p>\n

Review. 2: <\/strong><\/p>\n

\u00a0<\/strong>Hariz Zakka Muhammad et-al, 2018 were designed a speech recognization model wher speech was interpreting into indonesian language. They use the feature extraction method Mel Frequency Cepstral Coefficients (MFCC) and for this purpose they were used the \u201cMarkov Model\u201d For this purpose they use the following block diagram process. The complete process was divided into two parts first training and second is testing process. The execution method is shown in Fig3.<\/p>\n

According to Fig.3 first they create the data base of audio sound. Then do the audio signal processing to extract the features from the original signals.<\/p>\n

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They apply the k-means clustering algorithm on the featured data. It\u2019s an unsupervised algorithm. The recognition process is done by finding the likelihood value of all \u03bb models. The MFCC process has following steps:<\/p>\n