Interactive Voice Response Using Automatic Speech Recognition Techniques for Call Centers

2018 ◽  
Author(s):  
Rohit Raj Sehgal ◽  
Gaurav Raj
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Interactive Voice Response ◽  
Call Centers ◽  
Voice Response

Interactive Voice Response (IVR) Systems as Automatic Speech Recognition (ASR) Comes of Age

1998 ◽  
Vol 42 (4) ◽  
pp. 444-447 ◽  
Author(s):  
Daryle Gardner-Bonneau ◽  
Cristina Delogu ◽  
Chuck Green ◽  
Lydia Volaitis ◽  
Martha Lindeman ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Interactive Voice Response ◽  
Commercial Use ◽  
The Impact ◽  
Potential Customers ◽  
Workplace Speech ◽  
Ivr Systems ◽  
Made In ◽  
Voice Response

While interactive voice response (IVR) systems were rapidly making their way into the workplace, speech scientists were working hard to improve the performance of automatic speech recognition (ASR) systems to foster their acceptance among potential customers. In the last five years, great strides have been made in this regard, and the commercial use ASR is on the rise. The purpose of this panel is to explore the impact that ASR is (or is not) having on the design of IVR systems that were envisioned originally to operate solely via touch-tone input.

Estimation of ASR Parameterization for Interactive System

2021 ◽  
Vol 10 (1) ◽  
pp. 28-40
Author(s):  
Mohamed Hamidi ◽  
Hassan Satori ◽  
Ouissam Zealouk ◽  
Naouar Laaidi
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Interactive Voice Response ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Interactive System ◽  
Interactive Applications ◽  
Speaker Independent ◽  
Spectral Coefficients

In this study, the authors explore the integration of speaker-independent automatic Amazigh speech recognition technology into interactive applications to extract data remotely from a distance database. Based on the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies, the authors built an interactive speech system to allow users to interact with the interactive system through voice commands. The hidden Markov models (HMMs), Gaussian mixture models (GMMs), and Mel frequency spectral coefficients (MFCCs) are used to develop a speech system based on the ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.

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