Digital signal processing algorithms for automatic voice recognition

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms

Automatic voice recognition using traditional and artificial neural network approaches

The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time

FPGA Implementation of an Adaptive Noise Canceller for Robust Speech Enhancement Interfaces

This paper describes the design and implementation results of an adaptive Noise Canceller useful for the construction of Robust Speech Enhancement Interfaces. The algorithm being used has very good performance for real time applications. Its main disadvantage is the requirement of calculating several operations of division, having a high computational cost. Besides that, the accuracy of the algorithm is critical in fixed-point representation due to the wide range of the upper and lower bounds of the variables implied in the algorithm. To solve this problem, the accuracy is studied and according to the results obtained a specific word-length has been adopted for each variable. The algorithm has been implemented for Altera and Xilinx FPGAs using high level synthesis tools. The results for a fixed format of 40 bits for all the variables and for a specific word-length for each variable are analyzed and discussed

Эффективность реализации кросс-платформенных систем распознавания речи

Статья посвящена поиску наиболее эффективной реализации систем распознавания речи на различных вычислительных платформах и формированию базы данных и знаний акустического, фонетического и лексического уровней. Моделируется связь акустической и лингвистической компонент системы распознавания речевого сигнала, исследуется эффективность выбора речевых элементов. Описаны особенности реализации системы распознавания на архитектуре микропроцессоров цифровой обработки сигналов и возможность удаленной обработки речевого сигнала.The paper is devoted to finding the most effective speech recognition system implementation for a variety of computing platforms. Particular attention is given to the data and knowledge base forming for acoustic, phonetic and lexical levels. Relation between speech recognition acoustic and linguistic components is being modeled as well as spoken element selection has been investigated. Aspects of decoder implementation on the DSP microprocessor architecture including the possibility of speech signal remote processing are described.Статтю присвячено пошуку найбільш ефективної реалізації систем розпізнавання мовлення на різних обчислювальних платформах та формуванню бази даних і знань акустичного, фонетичного та лексичного рівнів. Моделюється зв’язок акустичної та лінгвістичної компонент системи розпізнавання мовленнєвого сигналу, досліджується ефективність вибору мовленнєвих елементів. Описано особливості реалізації системи розпізнавання на архітектурі мікропроцесорів цифрового оброблення сигналів і можливість віддаленої обробки мовленнєвого сигналу