DNN-based uncertainty estimation for weighted DNN-HMM ASR

2017/05/29 by José Nóvoa, Novoa, José, Josué Fredes +3
Computer Science · #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.1705.10368

openalex publication_date 2017/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, the uncertainty is defined as the mean square error between a given enhanced noisy observation vector and the corresponding clean one. Then, a DNN is trained by using enhanced noisy observation vectors as input and the uncertainty as output with a training database. In testing, the DNN receives an enhanced noisy observation vector and delivers the estimated uncertainty. This uncertainty in employed in combination with a weighted DNN-HMM based speech recognition system and compared with an existing estimation of the noise cancelling uncertainty variance based on an additive noise model. Experiments were carried out with Aurora-4 task. Results with clean, multi-noise and multi-condition training are presented.

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