Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition

In this work, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker verification (SV) system. We start our approach by carefully designing a data augmentation process to cover wide range of acoustic conditions and 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 a denoising autoencoder (mapping noisy and reverberated speech to its clean version) as well as an x-vector extractor which is currently considered as state-of-the-art in SV. Later, we use the autoencoder as a preprocessing step for text-independent SV system. 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 conclude that the proposed preprocessing can significantly improve both i-vector and x-vector baselines and that this technique can be used to build a robust SV system for various target domains.Comment: 16 pages, 7 figures, Submission to Computer Speech and Language, special issue on Speaker and language characterization and recognitio

A Snack Implementation and Tcl/Tk Interface to the Fundamental Frequency Variation Spectrum Algorithm

Intonation is an important aspect of vocal production, used for a variety of communicative needs. Its modeling is therefore crucial in many speech understanding systems, particularly those requiring inference of speaker intent in real-time. However, the estimation of pitch, traditionally the first step in intonation modeling, is computationally inconvenient in such scenarios. This is because it is often, and most optimally, achieved only after speech segmentation and recognition. A consequence is that earlier speech processing components, in today’s state-of-the-art systems, lack intonation awareness by fiat; it is not known to what extent this circumscribes their performance. In the current work, we present a freely available implementation of an alternative to pitch estimation, namely the computation of the fundamental frequency variation (FFV) spectrum, which can be easily employed at any level within a speech processing system. It is our hope that the implementation we describe aid in the understanding of this novel acoustic feature space, and that it facilitate its inclusion, as desired, in the front-end routines of speech recognition, dialog act recognition, and speaker recognition systems. 1