Analysis and comparison of recent MLP features for LVCSR systems
Fabio Valente, Mathew Magimai.-Doss, Wen Wang · 2011
MLP based front-ends have evolved in different ways in re-cent years beyond the seminal TANDEM-PLP features. This paper aims at providing a fair comparison of these recent pro-gresses including the use of different long/short temporal in-puts (PLP,MRASTA,wLP-TRAPS,DCT-TRAPS) and the use of complex architectures (bottleneck, hierarchy, multistream) that go beyond the conventional three layer MLP. Furthermore, the paper identifies which of these actually provide advantages over the conventional TANDEM-PLP. The investigation is car-ried on an LVCSR task for recognition of Mandarin Broadcast speech and results are analyzed in terms of Character Error Rate and phonetic confusions. Results reveal that as stand alone features, multistream front-ends can outperform by 10 % con-ventional MFCC while TANDEM-PLP only improve by 1 %. On the other hand, when used in concatenation with MFCC features, hierarchical/bottleneck front-ends reduce the character error rate by +18 % relative compared to +14 % relative from TANDEM-PLP. The various input long-term representations re-cently developed provide comparable performances.