Description

An Artificial Neural Network (ANN) is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. Early attempts at speech processing and recognition were primarily focused on understanding a handful of simple phonetic elements such as vowels. In 1952, three researchers at Bell Labs, Stephen. Balasko, R. Biddulph, and K. H. Davis, developed a system that could recognize digits spoken by a single speaker. Pioneering works in field of speech recognition using analysis of its spectrum were reported in 1940s. LINEAR PREDICTIVE CODING (LPC), a speech processing algorithm, was first proposed by Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone (NTT) in 1966. Further developments in LPC technology were made by Bishnu S. Atal and Manfred R. Schroeder at Bell Labs during the 1970s. LPC was the basis for voice-over-IP (VoIP) technology, as well as speech synthesizer chips, such as the Texas Instruments LPC Speech Chips used in the Speak & Spell toys from 1978. One of the first commercially available speech recognition products was Dragon Dictate, released in 1990. In 1992, technology developed by Lawrence Rabiner and others at Bell Labs was used by AT&T in their Voice Recognition Call Processing service to route calls without a human operator. By this point, the vocabulary of these systems was larger than the average human vocabulary. By the early 2000s, the dominant speech processing strategy started to shift away from Hidden Markov Models towards more modern neural networks and deep learning.

Interactive voice response (IVR) is a technology that allows telephone users to interact with a computer-operated telephone system through the use of voice and DTMF tones input with a keypad. In telecommunications, IVR allows customers to interact with a company's host system via a telephone keypad or by speech recognition, after which services can be inquired about through the IVR dialogue. IVR systems can respond with pre-recorded or dynamically generated audio to further direct users on how to proceed. IVR systems deployed in the network are sized to handle large call volumes and also used for outbound calling as IVR systems are more intelligent than many predictive dialer systems. IVR systems can be used stand-alone to create caller self-service solutions for instance for mobile purchases, banking payments, services, retail orders, utilities, travel information and weather conditions. In combination with systems such an automated attendant and ACD, call routing can be highly optimised for a better caller experience and workforce efficiency. A common misconception refers to an automated attendant as an IVR. The terms are distinct and mean different things to traditional telecommunications professionals the purpose of an IVR is to take input, process it, and return a result, whereas that of an automated attendant is to route calls. Having said that, since commercially available call handling systems cover both functionalities, they are often referred to as IVR. The term Voice Response Unit (VRU) is sometimes used as well

Thanks&regards                              

John Greesham

Journal coordinator

International Journal of innovative research in computer and communication engineering