Amr El-Desoky Mousa

Zixing Zhang, Jürgen Geiger, Jouni Pohjalainen et al.

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.

Amr El-Desoky Mousa, Erik Marchi, Björn Schuller

This paper presents our contribution to the 3rd CHiME Speech Separation and Recognition Challenge. Our system uses Bidirectional Long Short-Term Memory (BLSTM) Recurrent Neural Networks (RNNs) for Single-channel Speech Enhancement (SSE). Networks are trained to predict clean speech as well as noise features from noisy speech features. In addition, the system applies two methods of dereverberation on the 6-channel recordings of the challenge. The first is the Phase-Error based Filtering (PEF) that uses time-varying phase-error filters based on estimated time-difference of arrival of the speech source and the phases of the microphone signals. The second is the Correlation Shaping (CS) that applies a reduction of the long-term correlation energy in reverberant speech. The Linear Prediction (LP) residual is processed to suppress the long-term correlation. Furthermore, the system employs a LSTM Language Model (LM) to perform N-best rescoring of recognition hypotheses. Using the proposed methods, an improved Word Error Rate (WER) of 24.38% is achieved over the real eval test set. This is around 25% relative improvement over the challenge baseline.