Journal article

NOVOTNÝ Ondřej, PLCHOT Oldřich, GLEMBEK Ondřej, ČERNOCKÝ Jan and BURGET Lukáš. Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition. Computer Speech and Language. Amsterdam: Elsevier Science, 2019, vol. 2019, no. 58, pp. 403-421. ISSN 0885-2308. Available from:
Publication language:english
Original title:Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition
Title (cs):Analýza čištění signálu pomocí DNN pro robustní rozpoznávání mluvčího
Journal:Computer Speech and Language, Vol. 2019, No. 58, Amsterdam, NL
Speakerverification; Signalenhancement; Autoencoder; Neuralnetwork; Robustness; Embedding
In this work, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. Thetarget application is a robust speaker verification (SV) system. We start our approach by carefully designing a data augmentationprocess to cover a wide range of acoustic conditions and to obtain rich training data for various components of our SV system.We augment several well-known databases used in SV with artificially noised and reverberated data and we use them to train adenoising autoencoder (mapping noisy and reverberated speech to its clean version) as well as an x-vector extractor which is cur-rently considered as state-of-the-art in SV. Later, we use the autoencoder as a preprocessing step for a text-independent SV sys-tem. We compare results achieved with autoencoder enhancement, multi-condition PLDA training and their simultaneous use.We present a detailed analysis with various conditions of NIST SRE 2010, 2016, PRISM and with re-transmitted data. We con-clude that the proposed preprocessing can significantly improve both i-vector and x-vector baselines and that this technique canbe used to build a robust SV system for various target domains.
   author = {Ond{\v{r}}ej Novotn{\'{y}} and Old{\v{r}}ich
	Plchot and Ond{\v{r}}ej Glembek and Jan
	{\v{C}}ernock{\'{y}} and Luk{\'{a}}{\v{s}} Burget},
   title = {Analysis of DNN Speech Signal Enhancement for
	Robust Speaker Recognition},
   pages = {403--421},
   journal = {Computer Speech and Language},
   volume = 2019,
 number = 58,
   year = 2019,
   ISSN = {0885-2308},
   doi = {10.1016/j.csl.2019.06.004},
   language = {english},
   url = {}

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