to give users of "O+" calls the of call they wish to make.'cular training set to assess the both template-based and ta collected over the DDD upwards of 75,000 caller -F1 recording equipment.using algorithms currently n accuracy of 99.7% on the these and other experiments will be given in this paper.ion elopment of a speaker-independent speech recognition performs well over dialed-up telephone lines has been a goal of AT&T Bell Laboratories for over a decade [1,21. However, until recently most of our evaluations of recognition systems have been based on laboratory recorded data. These conditions typically consisted of cooperative subjects using local dialed-up lines over a Private Branch Exchange (PBX). Peak signal-to-noise ratios under these conditions generally ranged from 40 -60 dB. Using such local switched lines, the performance of the speech recognition algorithms tested was found to be quite good for a wide range of vocabulary sizes and complexities and for a wide range of talkers.Given the success of laboratory studies, an effort was made to test the viability of our speaker independent, isolated word recognition very large telephone customer populations [3,41. These rmed in 1982 and 1984) were conducted under 'real world' i.e. asking telephone customers to speak their telephone as a series of isolated digits) in a home environment over dialed-up telephone lines while trying to place a normal call. Under these conditions, signal-to-noise ratios of dB and 60 dB were observed. Extensive testing of this r network trials, the recognition algorithms evaluated te-based dynamic time-warping techniques using an lihood distortion measure. Since these tests were introduction of hidden Markov model speech rithms has greatly increased speech recognition procedures [5,61 and new distortion measures [8,9l have also increased database led to a 90% word recognition accuracy.

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The art and science ofspeech recognition research have advanced to the point where it is now possible to communicate reliably with a machine via telephone to carry outsimple tasks. However, the creation ofrobust algorithms is only partofthe overall process ofmaking speech recognition technology a commercial reality. In this paper, we present a brief overview ofspeech recognition technology and discuss the implementation ofthe algorithms with digital signal processing chips. In addition, we will show how theory and practice come together in real-world conditions bydescribing a telephone network trial ofautomatic credit card verification using speech recognition technology.

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