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Previous studies support the idea of merging auditory-based Gabor features with deep learning architectures to achieve robust automatic speech recognition, however, the cause behind the gain of such combination is still unknown. We believe these representations provide the deep learning decoder with more discriminable cues. Our aim with this paper is to validate this hypothesis by performing experiments with three different recognition tasks (Aurora 4, CHiME 2 and CHiME 3) and assess the discriminability of the information encoded by Gabor filterbank features. Additionally, to identify the contribution of low, medium and high temporal modulation frequencies subsets of the Gabor filterbank were used as features (dubbed LTM, MTM and HTM, respectively). With temporal modulation frequencies between 16 and 25 Hz, HTM consistently outperformed the remaining ones in every condition, highlighting the robustness of these representations against channel distortions, low signal-to-noise ratios and acoustically challenging real-life scenarios with relative improvements from 11 to 56% against a Mel-filterbank-DNN baseline. To explain the results, a measure of similarity between phoneme classes from DNN activations is proposed and linked to their acoustic properties. We find this measure to be consistent with the observed error rates and highlight specific differences on phoneme level to pinpoint the benefit of the proposed features. [ABSTRACT FROM AUTHOR]

Auto-Associative Neural Network (AANN) is a fully connected feed-forward neural network, trained to reconstruct its input at its output through a hidden compression layer. AANNs are used to model speakers in speaker verification, where a speaker-specific AANN model is obtained by adapting (or retraining) the Universal Background Model (UBM) AANN, an AANN trained on multiple held out speakers, using corresponding speaker data. When the amount of speaker data is limited, this adaptation procedure leads to overfitting. Additionally, the resultant speaker-specific parameters become noisy due to outliers in data. Thus, we propose to regularize the parameters of an AANN during speaker adaptation. A closed-form expression for updating the parameters is derived. Further, these speaker-specific AANN parameters are directly used as features in linear discriminant analysis (LDA)/probabilistic discriminant (PLDA) analysis based speaker verification system. The proposed speaker verification system outperforms the previously proposed weighted least squares (WLS) based AANN speaker verification system on NIST-08 speaker recognition evaluation (SRE). Moreover, the proposed speaker verification system obviates the need for an intermediate dimensionality reduction (or i-vector extraction) step. [ABSTRACT FROM PUBLISHER]