Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments

By Andreas Schwarz, Christian Huemmer, Roland Maas, Walter Kellermann. 2014

38 citations recorded by the source corpus.

We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, and represents the relative amount of diffuse noise in each time and frequency bin. It is shown that using the diffuseness feature as an additional input to a DNN-based acoustic model leads to a reduced word error rate for the REVERB challenge corpus, both compared to logmelspec features extracted from noisy signals, and features enhanced by spectral subtraction.

Subfields: Artificial Intelligence, Speech & Audio, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Speech & Audio

In Signal Processing

Citations in the corpus

The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, background): “…at multi-microphone front-end processing level in order to feed ASR with an enhanced speech input [4, 5, 6, 7, 8], the performance loss observed from close-talking to distant-speech remains quite critical, even when the most advanced DNN-based backend frameworks are adopted [9, 10, 11, 12, 13, 14].”
Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, background): “Concerning the latter field, the research community is currently experiencing a revolution due to the introduction of Deep Neural Networks (DNNs) [13], which have consistently outperformed previous Gaussian Mixture Models (GMMs) for both close [14] and distant-talking scenarios [15, 16, 17, 18, 19].”
Robust sound event detection in bioacoustic sensor networks (2019, methodology): “To this effect, they rely on auxiliary features that characterize spatial diffuseness [60], and are derived from a stereophonic audio input.”
Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, background): “…separation [4], speech dereverberation [5], speaker localization [6], acoustic event detection [7] as well as acoustic modeling [8, 9, 10, 11, 12], DSR still exhibits a lack of robustness, especially when adverse acoustic conditions originated by non-stationary noises and acoustic…”
Batch-normalized joint training for DNN-based distant speech recognition (2016, background): “Nevertheless, the recent success of deep learning has not only largely contributed to the substantial improvement of the speech recognition part of a DSR system [19, 20, 21, 22, 23, 24, 25], but has also enabled the de-a r X i v : 1703 .”