Voice Translator Based on Associative Memories

Abstract

An associative memory is a particular type of neural network for recalling output patterns from input patterns that might be altered by noise. During the last 50 years, several associative models have emerged and they only have been applied to solve problems where input patterns are images. Most of these models have several constraints that limit their applicability in complex problems. Recently in [13] it was introduced a new associative model based on some aspects of the human brain. This model is robust under different type of noises and image transformations, and useful in complex problems such as face and 3d object recognition. In this paper we adopt this model and apply it to problems that not involve images patterns, we applied to speech recognition problems. In this paper it is described a novel application where an associative memory works as a voice translator device performing a speech recognition process. In order to achieve this, the associative memory is trained using a corpus of 40 English words with their corresponding translation to Spanish. Each association used during training phase is composed by a voice signal in English and a voice signal in Spanish. Once trained our English-Spanish translator, when a voice signal in English is used to stimulate the associative memory we expect that the memory recalls the corresponding voice signal in Spanish. In order to test the accuracy of the proposal, a benchmark of 14500 altered versions of the original voice signals were used.