Dereverberation Of Autoregressive Envelopes For Far-field Speech Recognition

Abstract

The task of speech recognition in far-field environments is adversely affected by the reverberant artifacts that elicit as the temporal smearing of the sub-band envelopes. In this paper, we develop a neural model for speech dereverberation using the long-term sub-band envelopes of speech. The sub-band envelopes are derived using frequency domain linear prediction (FDLP) which performs an autoregressive estimation of the Hilbert envelopes. The neural dereverberation model estimates the envelope gain which when applied to reverberant signals suppresses the late reflection components in the far-field signal. The dereverberated envelopes are used for feature extraction in speech recognition. Further, the sequence of steps involved in envelope dereverberation, feature extraction and acoustic modeling for ASR can be implemented as a single neural processing pipeline which allows the joint learning of the dereverberation network and the acoustic model. Several experiments are performed on the

Stats

• citations7
• S2 citations—
• github stars0
• HF likes0
• heat score6.77
• arxiv keypurushothaman2021dereverberation

Related papers

• Deep Learning Based Dereverberation Of Temporal Envelopesfor Robust Speech Recognition (2020)5.84
• End-to-end Speech Recognition With Joint Dereverberation Of Sub-band Autoregressive Envelopes (2021)4.52
• Ensemble Of Jointly Trained Deep Neural Network-based Acoustic Models For Reverberant Speech Recognition (2016)0.00
• Improved Far-field Speech Recognition Using Joint Variational Autoencoder (2022)0.00
• Convolutive Prediction For Monaural Speech Dereverberation And Noisy-reverberant Speaker Separation (2021)11.39
• End-to-end Far-field Speech Recognition With Unified Dereverberation And Beamforming (2020)10.61
• Frequency Domain Multi-channel Acoustic Modeling For Distant Speech Recognition (2019)9.92
• Run-time Adaptation Of Neural Beamforming For Robust Speech Dereverberation And Denoising (2024)0.00