Feature Vector Selection to Improve ASR Robustness in Noisy Conditions

Contains fulltext : 75051.pdf (author's version ) (Open Access)4 p

Noise Reduction for Noise Robust Feature Extraction for Distributed Speech Recognition

Contains fulltext : 75048.pdf (author's version ) (Open Access)4 p

Towards improving ASR robustness for PSN and GSM telephone applications

In real-life applications, errors in the speech recognition system are mainly due to inefficient detection of speech Z. segments, unreliable rejection of Out-Of-Vocabulary OOV words, and insufficient account of noise and transmission channel effects. In this paper, we review a set of techniques developed at CNET in order to increase the robustness to mismatches between training and testing conditions. These techniques are divided in two classes: preprocessing techniques Z. and Hidden Markov Models HMM parameters adaptation. The results of several experiments carried out on field databases, as well as on databases collected over PSN and GSM networks are presented. The main sources of errors are analyzed. We show that a blind equalization scheme significantly improves the recognition accuracy regarding both field and GSM data. Speech detection allows a system to delimit the boundaries of the words to be recognized. We also use preprocessing techniques to increase the robustness of such..