DNN-based performance measures for predicting error rates in automatic speech recognition and optimizing hearing aid parameters
- authored by
- A.M. Castro Martinez, Lukas Gerlach, Guillermo Payá-Vayá, Hynek Hermansky, Jasper Ooster, Bernd T. Meyer
- Abstract
In several applications of machine listening, predicting how well an automatic speech recognition system will perform before the actual decoding enables the system to adapt to unseen acoustic characteristics dynamically. Feedback about speech quality, for instance, could allow modern hearing aids to select a speech source in complex acoustic scenes with the aim of enhancing the speech intelligibility of a target speaker. In this study, we look at different performance measures to estimate the word error rates of simulated behind-the-ear hearing aid signals and detect the azimuth angle of the target source in 180-degree spatial scenes. These measures derive from phoneme posterior probabilities produced by a deep neural network acoustic model. However, the more complex the model is, the more computationally expensive it becomes to obtain these measures; therefore, we assess how the model size affects prediction performance. Our findings suggest measures derived from smaller nets are suitable to predict error rates of more complex models reliably enough to be implemented in hearing aid hardware.
- Organisation(s)
-
Architectures and Systems Section
- External Organisation(s)
-
Cluster of Excellence Hearing4all
Carl von Ossietzky University of Oldenburg
Johns Hopkins University
- Type
- Article
- Journal
- Speech communication
- Volume
- 106
- Pages
- 44-56
- No. of pages
- 13
- ISSN
- 0167-6393
- Publication date
- 01.2019
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Software, Modelling and Simulation, Communication, Language and Linguistics, Linguistics and Language, Computer Vision and Pattern Recognition, Computer Science Applications
- Sustainable Development Goals
- SDG 3 - Good Health and Well-being
- Electronic version(s)
-
https://doi.org/10.1016/j.specom.2018.11.006 (Access:
Closed)
