Comparison of Noise Robust Methods in Large Vocabulary Speech Recognition

Reference:

Sami Keronen, Ulpu Remes, Kalle Palomäki, Tuomas Virtanen, and Mikko Kurimo. Comparison of noise robust methods in large vocabulary speech recognition. In Proceedings of the 18th European Signal Processing Conference, EUSIPCO 2010, Aalborg, Denmark, August 2010.

Abstract:

In this paper, a comparison of three fundamentally different noise robust approaches is carried out. The recognition performances of multicondition training, Data-driven Parallel Model Combination (DPMC), and cluster-based missing data reconstruction methods implemented in a large vocabulary continuous speech recognition system are evaluated with Finnish language speech data consisting of real recordings in noisy environments. All three methods improve the recognition accuracy substantially in poor signal-to-noise ratio (SNR) conditions when compared to a baseline system trained on clean speech. DPMC and missing data reconstruction systems give the best performance on high SNR conditions. On low SNR conditions, the performance of multicondition trained system is ranked the best, DPMC the second best and missing data reconstruction the third.

Keywords:

noise robust, speech recognition, DPMC

Suggested BibTeX entry:

@inproceedings{keronen10.eusipco,
    address = {Aalborg, Denmark},
    author = {Sami Keronen and Ulpu Remes and Kalle Palom\"{a}ki and Tuomas Virtanen and Mikko Kurimo},
    booktitle = {Proceedings of the 18th European Signal Processing Conference, EUSIPCO 2010},
    month = {August},
    title = {Comparison of Noise Robust Methods in Large Vocabulary Speech Recognition},
    year = {2010},
}