Performance Evaluation of The Cepstral-Based STPS Features with Res-TCNetworks for ASR in Adverse Environments
Aadel Mohammed Alatwi · 2020 International Conference on Computing and Information Technology (ICCIT-1441) · 2020
In this paper, we evaluate the performance of Automatic speech recognition (ASR) using cepstral-based Smoothed and Thresholded Power Spectrum (STPS-LP) features with residual Temporal Convolution Networks (Res-TCN). The experiments were performed in comparison with cepstral-based LP features, where both features were obtained from AMR bit-stream parameters. In terms of Word Error Rate (WER%), results show that using the STPS-LP cepstral features, the performance of speech recognition was on average 6.22% better in comparison with the conventional LP cepstral features, depending on the type of environmental noise.