Denoising and Raw-waveform Networks for Weakly-Supervised Gender Identification on Noisy Speech

Abstract

This paper presents a raw-waveform neural network and uses it along with a denoising network for clustering in weaklysupervised learning scenarios under extreme noise conditions. Specifically, we consider language independent Automatic Gender Recognition (AGR) on a set of varied noise conditions and Signal to Noise Ratios (SNRs). We formulate the denoising problem as a source separation task and train the system using a discriminative criterion in order to enhance output SNRs. A denoising Recurrent Neural Network (RNN) is first trained on a small subset (roughly one-fifth) of the data for learning a speechspecific mask. The denoised speech signal is then directly fed as input to a raw-waveform convolutional neural network (CNN) trained with denoised speech. We evaluate the standalone performance of denoiser in terms of various signal-to-noise measures and discuss its contribution towards robust AGR. An absolute improvement of 11.06% and 13.33% is achieved by the combined pipeline over the i-vector SVM baseline system for 0 dB and-5 dB SNR conditions, respectively. We further analyse the information captured by the first CNN layer in both noisy and denoised speech.

On Learning to Identify Genders from Raw Speech Signal Using CNNs

“…In recent years, features such as i-vectors have been used for gender recognition [10]. In [26], a comparison between the proposed CNN-based approach and ivector based approach has been investigated for identifying gender under noisy conditions. It has been found that in both noisy condition training and denoised condition training the proposed CNN-based approach yields significantly better system.…”