TY - JOUR
T1 - Ensemble hierarchical extreme learning machine for speech dereverberation
AU - Hussain, Tassadaq
AU - Siniscalchi, Sabato Marco
AU - Wang, Hsiao Lan Sharon
AU - Tsao, Yu
AU - Salerno, Valerio Mario
AU - Liao, Wen Hung
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Data-driven deep learning solutions with gradient-based neural architecture, have proven useful in overcoming some limitations of traditional signal processing techniques. However, a large number of reverberant-anechoic training utterance pairs covering as many environmental conditions as possible is required to achieve robust dereverberation performance in unseen testing conditions. In this article, we propose to address the data requirement issue while preserving the advantages of deep neural structures leveraging upon hierarchical extreme learning machines (HELMs), which are not gradient-based neural architectures. In particular, an ensemble HELM learning framework is established to effectively recover anechoic speech from a reverberant one based on spectral mapping. In addition to the ensemble learning framework, we further derive two novel HELM models, namely, highway HELM [HELM(Hwy)] and residual HELM [HELM(Res)], both incorporating low-level features to enrich the information for spectral mapping. We evaluated the proposed ensemble learning framework using simulated and measured impulse responses by employing Texas Instrument and Massachusetts Institute of Technology (TIMIT), Mandarin hearing in noise test (MHINT), and reverberant voice enhancement and recognition benchmark (REVERB) corpora. The experimental results show that the proposed framework outperforms both traditional methods and a recently proposed integrated deep and ensemble learning algorithm in terms of standardized objective and subjective evaluations under matched and mismatched testing conditions for simulated and measured impulse responses.
AB - Data-driven deep learning solutions with gradient-based neural architecture, have proven useful in overcoming some limitations of traditional signal processing techniques. However, a large number of reverberant-anechoic training utterance pairs covering as many environmental conditions as possible is required to achieve robust dereverberation performance in unseen testing conditions. In this article, we propose to address the data requirement issue while preserving the advantages of deep neural structures leveraging upon hierarchical extreme learning machines (HELMs), which are not gradient-based neural architectures. In particular, an ensemble HELM learning framework is established to effectively recover anechoic speech from a reverberant one based on spectral mapping. In addition to the ensemble learning framework, we further derive two novel HELM models, namely, highway HELM [HELM(Hwy)] and residual HELM [HELM(Res)], both incorporating low-level features to enrich the information for spectral mapping. We evaluated the proposed ensemble learning framework using simulated and measured impulse responses by employing Texas Instrument and Massachusetts Institute of Technology (TIMIT), Mandarin hearing in noise test (MHINT), and reverberant voice enhancement and recognition benchmark (REVERB) corpora. The experimental results show that the proposed framework outperforms both traditional methods and a recently proposed integrated deep and ensemble learning algorithm in terms of standardized objective and subjective evaluations under matched and mismatched testing conditions for simulated and measured impulse responses.
KW - Ensemble learning
KW - hierarchical extreme learning machines (HELMs)
KW - highway extreme learning machine
KW - residual extreme learning machine
KW - speech dereverberation
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U2 - 10.1109/TCDS.2019.2953620
DO - 10.1109/TCDS.2019.2953620
M3 - Article
AN - SCOPUS:85075351012
SN - 2379-8920
VL - 12
SP - 744
EP - 758
JO - IEEE Transactions on Cognitive and Developmental Systems
JF - IEEE Transactions on Cognitive and Developmental Systems
IS - 4
M1 - 8906014
ER -