An assessment of automatic speech recognition as speech intelligibility estimation in the context of additive noise

2006 ◽  
Author(s):  
Wei M. Liu ◽  
John S. D. Mason ◽  
Nicholas W. D. Evans ◽  
Keith A. Jellyman
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Intelligibility ◽  
Additive Noise

scholarly journals Comparative Evaluation of Single-Channel MMSE-Based Noise Reduction Schemes for Speech Recognition

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Emanuele Principi ◽  
Simone Cifani ◽  
Rudy Rotili ◽  
Stefano Squartini ◽  
Francesco Piazza
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Noise Reduction ◽  
Automatic Speech Recognition ◽  
Comparative Evaluation ◽  
Scientific Community ◽  
Additive Noise ◽  
Single Channel ◽  
Acoustic Conditions

One of the big challenges in the field of Automatic Speech Recognition (ASR) consists in developing suitable solutions able to work properly also in adverse acoustic conditions, like in presence of additive noise and/or in reverberant rooms. Recently a certain attention has been paid to deeply integrate the noise suppressor in the feature extraction pipeline. In this paper, different single-channel MMSE-based noise reduction schemes have been implemented both in the frequency and cepstral domains and the related recognition performances evaluated on the AURORA2 and AURORA4 databases, therefore providing a useful reference for the scientific community.

The use of automatic speech recognition showing the influence of nasality on speech intelligibility

2010 ◽  
Vol 267 (11) ◽  
pp. 1719-1725 ◽  
Author(s):  
S. Mayr ◽  
K. Burkhardt ◽  
M. Schuster ◽  
K. Rogler ◽  
A. Maier ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Intelligibility

scholarly journals Automatic Speech Recognition in Noise for Parkinson's Disease: A Pilot Study

2021 ◽  
Vol 4 ◽  
Author(s):  
Alireza Goudarzi ◽  
Gemma Moya-Galé
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Recognition ◽  
Pilot Study ◽  
Automatic Speech Recognition ◽  
Speech Intelligibility ◽  
Real Life ◽  
The Past ◽  
Noisy Signals ◽  
Speech Recognition In Noise

The sophistication of artificial intelligence (AI) technologies has significantly advanced in the past decade. However, the observed unpredictability and variability of AI behavior in noisy signals is still underexplored and represents a challenge when trying to generalize AI behavior to real-life environments, especially for people with a speech disorder, who already experience reduced speech intelligibility. In the context of developing assistive technology for people with Parkinson's disease using automatic speech recognition (ASR), this pilot study reports on the performance of Google Cloud speech-to-text technology with dysarthric and healthy speech in the presence of multi-talker babble noise at different intensity levels. Despite sensitivities and shortcomings, it is possible to control the performance of these systems with current tools in order to measure speech intelligibility in real-life conditions.

Sign in / Sign up

Export Citation Format

Share Document