A media-specific FEC based on huffman coding for distributed speech recognition

In this paper, we propose a media-specific forward error correction (FEC) method based on Huffman coding for distributed speech recognition (DSR). In order to mitigate the performance degradation of DSR in noisy channel environments, the importance of each subvector for the DSR system is first explored. As a result, the first subvector information for the mel-frequency cepstral coefficients (MFCCs) is then added as an error protection code. At the same time, Huffman coding methods are applied to compressed MFCCs to prevent the bit-rate increase by using such protection codes,. Different Huffman trees for MFCCs are designed according to the voicing class, subvector-wise, and their combinations. It is shown from the recognition experiments on the Aurora 4 large vocabulary database under several noisy channel conditions that the proposed FEC method is able to achieve the relative average word error rate (WER) reduction by 9.03~17.81% compared with the standard DSR system using no FEC methods.