TR01: Time-continuous Sparse Imputation

TL;DR

This paper introduces a novel time-continuous sparse imputation method for noise-robust speech recognition, leveraging large time-context information through a sliding window and sparse representations of reliable features.

Contribution

It presents a new approach that exploits large time-context for speech imputation, improving noise robustness over previous frame-by-frame methods.

Findings

Effective noise robustness demonstrated on AURORA-2 database.

Sparse representation approach improves speech feature estimation.

Potential for enhanced automatic speech recognition accuracy.

Abstract

An effective way to increase the noise robustness of automatic speech recognition is to label noisy speech features as either reliable or unreliable (missing) prior to decoding, and to replace the missing ones by clean speech estimates. We present a novel method to obtain such clean speech estimates. Unlike previous imputation frameworks which work on a frame-by-frame basis, our method focuses on exploiting information from a large time-context. Using a sliding window approach, denoised speech representations are constructed using a sparse representation of the reliable features in an overcomplete basis of fixed-length exemplar fragments. We demonstrate the potential of our approach with experiments on the AURORA-2 connected digit database.