REPORT Kabil_Idiap-RR-10-2022/IDIAP SPARSE AUTOENCODERS TO ENHANCE SPEECH RECOGNITION Kabil, Selen Hande Bourlard, Hervé Deep neural network high-dimensional sparse representations sparse autoencoder speech recognition EXTERNAL http://publications.idiap.ch/attachments/reports/2020/Kabil_Idiap-RR-10-2022.pdf PUBLIC Idiap-RR-10-2022 2022 Idiap August 2022 Submitted to ICASSP 2021 Starting from a state-of-the-art speech recognition system as baseline, we investigate the use of sparse autoencoders to fur- ther process the baseline acoustic model outputs (senone likelihoods) to obtain even more reliable senone and phone likelihoods with better classification performance. Inspired by dictionary learning, we use shallow overcomplete sparse autoencoders to project the senone likelihoods to high-dimensional sparse space, which is expected to increase the separability among different speech units while suppressing the distortions. On Augmented Multiparty Interaction (AMI) dataset with both Individual Head-mounted Microphone (IHM) and Single-Distant Microphone (SDM) conditions, we show that classifiers trained on the high-dimensional sparse representations from the sparse autoencoders improve senone and phone classification performance compared to the baseline, especially in noisy (SDM) condition. As for the word recognition, we obtain promising improvements when weighted combi- nation of the senone likelihoods from the baseline acoustic model and from our senone classifier is used for decoding.