scholarly journals Performing Text – To – Speech based Hidden Markov Model on Digital Signal Processing platform

2014 ◽  
Vol 17 (1) ◽  
pp. 32-38
Author(s):  
Nhat Truong Minh Vu ◽  
Binh Hieu Nguyen ◽  
Nhat Minh Pham ◽  
Thuan Huu Huynh ◽  
Tu Trong Bui ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hardware Implementation ◽  
Hidden Markov ◽  
Digital Signal ◽  
Text To Speech ◽  
Processing Platform

Text To Speech (TTS) using Hidden Markov Model (HMM) has become popular in recent years. However, because most of such systems were implemented on personal computers (PCs), it is difficult to offer these systems to real applications. In this paper, we present a hardware implementation of TTS based on DSP architecture, which is applicable for real applications. By optimizing hardware architecture, the quality of the DSP-based synthesized speech is nearly identical to that synthesized on PCs.

scholarly journals Aplikasi Pengenalan Suara untuk Pengamanan Software Komputer Menggunakan Metode MFCC(Mel Frequency Cepstrum Coefficients) dan HMM (Hidden Markov Model)

2020 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Ichbal Septian El Bashart ◽  
Triyanto Pangaribowo
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Digital Signal ◽  
Mel Frequency Cepstral Coefficient

Proses pengolahan sinyal suara dapat menggunakan digital signal processing dan algoritma tertentu yang dapat mengolah baik itu dengan fungsi matematis ataupun persamaan sehingga dapat dikenali. Suara sendiri memiliki sinyal informasi yang tidak terbatas dan memiliki banyak kegunaan penerapan sehari – hari termasuk diantaranya adalah untuk proses kontrol dan identifikasi aplikasi komputer misalnya. Perancangan aplikasi pengenalan suara untuk membuka aplikasi komputer berdasar pengguna nya dilakukan dengan 2 proses utama yaitu dengan menggunakan algoritma untuk ekstraksi pencocokan ciri. Ekstraksi ciri merupakan fitur untuk mendapatkan ciri atau elemen suara yang bisa membeda bedakan masing-masing suara manusia. Metode yang digunakan dalam tugas akhir ini untuk ekkstraksi suara adalah MFCC (Mel Frequency Cepstral Coefficient). Proses selanjutnya adalah pencocokan fitur ciri dengan menggunakan HMM (Hidden Markov Model). Hasil pengujian dari penelitian ini dapat diketahui apabila semakin adanya kemiripan suara saat pengujian dengan saat pelatihan maka aplikasi yang terbuka akan sesuai dengan aplikasi yang disuarakan. Hasil pengujian suara untuk mengenali suara yang ada pada database suara yang tertinggi 80% dan untuk suara diluar database hanya 0-40%, dengan kondisi aplikasi yang terbuka masih ada perbedaan antara aplikasi yang diinginkan dengan hasil keputusan sistem

scholarly journals A Novel Text to Speech Technique for Tamil Language using Hidden Markov Models (HMM)

2019 ◽  
Vol 8 (10) ◽  
pp. 38-47
Keyword(s):  
Signal Processing ◽  
Markov Model ◽  
Speech Processing ◽  
Major Part ◽  
Markov Models ◽  
Hidden Markov ◽  
Digital Signal ◽  
Text To Speech ◽  
Traditional System ◽  
Local Languages

Application of digital signal processing in speech processing plays a major part in our everyday life. Text to speech system lets people to see and read out loud consecutively. Text-to-speech synthesizers use synthesis techniques that require good quality speech. Text to speech conversion (TTS) can apply to many applications such as automation, audio recording and audio-based assistance system. Text to speech conversion can be applied for various multinational language as well as for a number of local languages. An efficient text to speech conversion for Tamil language with extreme accuracy is proposed in this work. Multi feature, with a Hidden Markov Model (HMM) predictor is used to convert text to speech efficiently. By using the proposed method, the precision of the framework is enhanced by a factor of 6% when contrasted with the traditional system.

Signal rectifying and detecting in the NI ELVIS complex

2021 ◽  
pp. 63-71
Author(s):  
E.B. Solovyeva ◽  
◽  
K.S. Ezerov ◽  
Yu.M. Inshakov ◽  
◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Electrical Engineering ◽  
Digital Signal ◽  
Nonlinear Circuit ◽  
Signal Conversion ◽  
Half Wave ◽  
Nonlinear Distortions ◽  
Visualization Tools

A half-wave diode rectifier is represented as a nonlinear circuit to study in a laboratory course of electrical engineering. The control and measurement complex NI ELVIS effectively displays the nonlinear circuit properties using oscillograms and spectrograms of signals. The coefficients of ripples and nonlinear distortions are calculated. They help to evaluate the influence of the circuit parameters on the quality of signal conversion when rectifying and detecting. The advantages of the NI ELVIS complex consist in, for instance, the real-time digital signal processing, the visualization of results, the ergonomic visualization tools. These advantages provide clarity and precision in the presentation of complex nonlinear processes in electrical engineering.

Simplified VHDL Coding of Modified Non-Restoring Square Root Calculator

2012 ◽  
Vol 1 (1) ◽  
pp. 37 ◽  
Author(s):  
Tole Sutikno ◽  
Aiman Zakwan Jidin ◽  
Auzani Jidin ◽  
Nik Rumzi Nik Idris
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Hardware Implementation ◽  
Digital Signal ◽  
Square Root ◽  
Main Principle ◽  
Hardware Resource ◽  
Root Operation ◽  
Hardware Designs

Square root calculation is one of the most useful and vital operation in digital signal processing which in recent generations of processors, the operation is performed by the hardware. The hardware implementation of the square root operation can be achieved by different means, but it is very dependent on programmer's sense and ability to write efficient hardware designs. This paper offers universal and shortest VHDL coding of modified non-restoring square root calculator. The main principle of the method is similar with conventional non-restoring algorithm, but it only uses subtract operation and append 01, while add operation and append 11 is not used. The strategy has conducted to implement successfully in FPGA hardware, and offer an efficient in hardware resource, and it is superior.

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