Cumulative Adaptation for BLSTM Acoustic Models
Table 5: Detailed WERs (in %) on Hub5’00 comparing the influence of i-vectors, SMBR, and environment adaptation with affine transformation (AT). WERs are given for count based language models and RNNLM.
['[BOLD] AT', '[BOLD] i-vec.', '[BOLD] SMBR', '[BOLD] LM', '[BOLD] Hub5’00  [BOLD] SWBD', '[BOLD] Hub5’00  [BOLD] CH', '[BOLD] Hub5’00  [BOLD] Avg.']
Similar to the second pass adaptation also SMBR training provides larger relative improvements if i-vectors are used in the baseline.