Frame recursive dynamic mean bias removal technique for robust environment-aware speech recognition in real world applications

Abstract

In this paper, we investigated and simulated the frame recursive dynamic mean bias removing technique in the cepstral domain with a time smoothing parameter in order to improve the robustness of automatic speech recognition (ASR) in realtime environments. The objective of this simulation was to examine the suitability of the frame recursive cepstral mean bias removal technique as a part of an effort to develop single channel joint additive noise and channel distortion compensation (JAC) algorithm in feature space for real-world applications. The Aurora2 speech corpus was used in this simulation. The simulation results show that the frame recursive dynamic mean bias removal technique performs better in real-time scenarios compared to conventional approaches (non real-time) to improve the robustness of ASR under noisy conditions.