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arXiv · 1803.09013

Exploring the robustness of features and enhancement on speech recognition systems in highly-reverberant real environments

José Novoa·Juan Pablo Escudero·Jorge Wuth·Victor Poblete·Simon King·Richard Stern·Néstor Becerra Yoma
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Abstract

This paper evaluates the robustness of a DNN-HMM-based speech recognition system in highly-reverberant real environments using the HRRE database. The performance of locally-normalized filter bank (LNFB) and Mel filter bank (MelFB) features in combination with Non-negative Matrix Factorization (NMF), Suppression of Slowly-varying components and the Falling edge (SSF) and Weighted Prediction Error (WPE) enhancement methods are discussed and evaluated. Two training conditions were considered: clean and reverberated (Reverb). With Reverb training the use of WPE and LNFB provides WERs that are 3% and 20% lower in average than SSF and NMF, respectively. WPE and MelFB provides WERs that are 11% and 24% lower in average than SSF and NMF, respectively. With clean training, which represents a significant mismatch between testing and training conditions, LNFB features clearly outperform MelFB features. The results show that different types of training, parametrization, and enhancement techniques may work better for a specific combination of speaker-microphone distance and reverberation time. This suggests that there could be some degree of complementarity between systems trained with different enhancement and parametrization methods.

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José Novoa, Juan Pablo Escudero, Jorge Wuth, Victor Poblete, Simon King, Richard Stern, Néstor Becerra Yoma. 2018-03-23. Exploring the robustness of features and enhancement on speech recognition systems in highly-reverberant real environments. https://arxiv.org/abs/1803.09013

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