Vietnamese speech recognition using Dynamic Time Warping and Coefficient of Correlation

2013 ◽  
Author(s):  
Vu Duc Lung ◽  
Vu N. Truong
Keyword(s):  
Speech Recognition ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Coefficient Of Correlation ◽  
Dynamic Time

Merge-Weighted Dynamic Time Warping for Speech Recognition

2014 ◽  
Vol 29 (6) ◽  
pp. 1072-1082 ◽  
Author(s):  
Xiang-Lilan Zhang ◽  
Zhi-Gang Luo ◽  
Ming Li
Keyword(s):  
Speech Recognition ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time

An Improved Method In Speech Signal Input Representation Based On DTW Technique For NN Speech Recognition System

2012 ◽  
Author(s):  
Rubita Sudirman ◽  
Sh. Hussain Salleh ◽  
Shaharuddin Salleh
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Conjugate Gradient ◽  
Template Matching ◽  
Dynamic Time Warping ◽  
Gradient Descent ◽  
Reference Frames ◽  
Time Warping ◽  
Dynamic Time ◽  
Quasi Newton

Kertas kerja ini membentangkan pemprosesan semula ciri pertuturan pemalar Pengekodan Ramalan Linear (LPC) bagi menyediakan template rujukan yang boleh diharapkan untuk set perkataan yang hendak dicam menggunakan rangkaian neural buatan. Kertas kerja ini juga mencadangkan penggunaan cirian kenyaringan yang ditakrifkan dari data pertuturan sebagai satu lagi ciri input. Algoritma Warping Masa Dinamik (DTW) menjadi asas kepada algoritma baru yang dibangunkan, ia dipanggil sebagai DTW padanan bingkai (DTW–FF). Algoritma ini direka bentuk untuk melakukan padanan bingkai bagi pemprosesan semula input LPC. Ia bertujuan untuk menyamakan bilangan bingkai input dalam set ujian dengan set rujukan. Pernormalan bingkaian ini adalah diperlukan oleh rangkaian neural yang direka untuk membanding data yang harus mempunyai kepanjangan yang sama, sedangkan perkataan yang sama dituturkan dengan kepanjangan yang berbeza–beza. Dengan melakukan padanan bingkai, bingkai input dan rujukan boleh diubahsuai supaya bilangan bingkaian sama seperti bingkaian rujukan. Satu lagi misi kertas kerja ini ialah mentakrif dan menggunakan cirian kenyaringan menggunakan algoritma penapis harmonik. Selepas kenyaringan ditakrif dan pemalar LPC dinormalkan kepada bilangan bingkaian dikehendaki, pengecaman pertuturan menggunakan rangkaian neural dilakukan. Keputusan yang baik diperoleh sehingga mencapai ketepatan setinggi 98% menggunakan kombinasi cirian DTW–FF dan cirian kenyaringan. Di akhir kertas kerja ini, perbandingan kadar convergence antara Conjugate gradient descent (CGD), Quasi–Newton, dan Steepest Gradient Descent (SGD) dilakukan untuk mendapatkan arah carian titik global yang optimal. Keputusan menunjukkan CGD memberikan nilai titik global yang paling optimal dibandingkan dengan Quasi–Newton dan SGD. Kata kunci: Warping masa dinamik, pernormalan masa, rangkaian neural, pengecaman pertuturan, conjugate gradient descent A pre–processing of linear predictive coefficient (LPC) features for preparation of reliable reference templates for the set of words to be recognized using the artificial neural network is presented in this paper. The paper also proposes the use of pitch feature derived from the recorded speech data as another input feature. The Dynamic Time Warping algorithm (DTW) is the back–bone of the newly developed algorithm called DTW fixing frame algorithm (DTW–FF) which is designed to perform template matching for the input preprocessing. The purpose of the new algorithm is to align the input frames in the test set to the template frames in the reference set. This frame normalization is required since NN is designed to compare data of the same length, however same speech varies in their length most of the time. By doing frame fixing, the input frames and the reference frames are adjusted to the same number of frames according to the reference frames. Another task of the study is to extract pitch features using the Harmonic Filter algorithm. After pitch extraction and linear predictive coefficient (LPC) features fixed to a desired number of frames, speech recognition using neural network can be performed and results showed a very promising solution. Result showed that as high as 98% recognition can be achieved using combination of two features mentioned above. At the end of the paper, a convergence comparison between conjugate gradient descent (CGD), Quasi–Newton, and steepest gradient descent (SGD) search direction is performed and results show that the CGD outperformed the Newton and SGD. Key words: Dynamic time warping, time normalization, neural network, speech recognition, conjugate gradient descent

scholarly journals FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF THE DYNAMIC TIME WARPING ALGORITHM FOR SPEECH RECOGNITION

2017 ◽  
Vol 10 (13) ◽  
pp. 248
Author(s):  
John Sahaya Rani Alex ◽  
Mitali Bhojwani
Keyword(s):  
Speech Recognition ◽  
Field Programmable Gate Array ◽  
Dynamic Time Warping ◽  
Recognition Algorithm ◽  
Consumer Electronics ◽  
Time Warping ◽  
Time To Market ◽  
Computing Power ◽  
Field Programmable ◽  
Dynamic Time

Objective of this research is to implement a speech recognition algorithm in smaller form factor device. Speech recognition is an extensively used inmobile and in numerous consumer electronics devices. Dynamic time warping (DTW) method which is based on dynamic programming is chosen tobe implemented for speech recognition because of the latest trend in evolving computing power. Implementation of DTW in field-programmable gatearray is chosen for its featured flexibility, parallelization and shorter time to market. The above algorithm is implemented using Verilog on Xilinx ISE.The warping cost is less if the similarity is found and is more for dissimilar sequences which is verified in the simulation output. The results indicatethat real time implementation of DTW based speech recognition could be done in future.

Building Sequence Kernels for Speaker Verification and Word Recognition

2011 ◽  
pp. 575-589
Author(s):  
Vincent Wan
Keyword(s):  
Speech Recognition ◽  
Speech Processing ◽  
Kernel Methods ◽  
Speaker Recognition ◽  
Dynamic Time Warping ◽  
Speaker Verification ◽  
Dimensional Space ◽  
Time Warping ◽  
Recognition Systems ◽  
Dynamic Time

This chapter describes the adaptation and application of kernel methods for speech processing. It is divided into two sections dealing with speaker verification and isolated-word speech recognition applications. Significant advances in kernel methods have been realised in the field of speaker verification, particularly relating to the direct scoring of variable-length speech utterances by sequence kernel SVMs. The improvements are so substantial that most state-of-the-art speaker recognition systems now incorporate SVMs. We describe the architecture of some of these sequence kernels. Speech recognition presents additional challenges to kernel methods and their application in this area is not as straightforward as for speaker verification. We describe a sequence kernel that uses dynamic time warping to capture temporal information within the kernel directly. The formulation also extends the standard dynamic time-warping algorithm by enabling the dynamic alignment to be computed in a high-dimensional space induced by a kernel function. This kernel is shown to work well in an application for recognising low-intelligibility speech of severely dysarthric individuals.

Building Sequence Kernels for Speaker Verification and Word Recognition

2011 ◽  
pp. 246-262
Author(s):  
Vincent Wan
Keyword(s):  
Speech Recognition ◽  
Speech Processing ◽  
Kernel Methods ◽  
Speaker Recognition ◽  
Dynamic Time Warping ◽  
Speaker Verification ◽  
Dimensional Space ◽  
Time Warping ◽  
Recognition Systems ◽  
Dynamic Time

This chapter describes the adaptation and application of kernel methods for speech processing. It is divided into two sections dealing with speaker verification and isolated-word speech recognition applications. Significant advances in kernel methods have been realised in the field of speaker verification, particularly relating to the direct scoring of variable-length speech utterances by sequence kernel SVMs. The improvements are so substantial that most state-of-the-art speaker recognition systems now incorporate SVMs. We describe the architecture of some of these sequence kernels. Speech recognition presents additional challenges to kernel methods and their application in this area is not as straightforward as for speaker verification. We describe a sequence kernel that uses dynamic time warping to capture temporal information within the kernel directly. The formulation also extends the standard dynamic time-warping algorithm by enabling the dynamic alignment to be computed in a high-dimensional space induced by a kernel function. This kernel is shown to work well in an application for recognising low-intelligibility speech of severely dysarthric individuals.

HUMAN-LIKE DYNAMIC PROGRAMMING NEURAL NETWORKS FOR DYNAMIC TIME WARPING SPEECH RECOGNITION

1995 ◽  
Vol 06 (01) ◽  
pp. 79-89 ◽  
Author(s):  
CHINCHUAN CHIU ◽  
MICHAEL A. SHANBLATT
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Speech Recognition ◽  
Dynamic Time Warping ◽  
Hardware Implementation ◽  
Time Warping ◽  
Network Method ◽  
The Neural Networks ◽  
Dynamic Time ◽  
Better Than

This paper presents a human-like dynamic programming neural network method for speech recognition using dynamic time warping. The networks are configured, much like human’s, such that the minimum states of the network’s energy function represent the near-best correlation between test and reference patterns. The dynamics and properties of the neural networks are analytically explained. Simulations for classifying speaker-dependent isolated words, consisting of 0 to 9 and A to Z, show that the method is better than conventional methods. The hardware implementation of this method is also presented.

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