The ICSTM+TUM+UP Approach to the 3rd CHIME Challenge: Single-Channel LSTM Speech Enhancement with Multi-Channel Correlation Shaping Dereverberation and LSTM Language Models
This work addresses speech recognition challenges in noisy settings like smart devices, but it is incremental as it builds on existing LSTM and dereverberation methods.
The paper tackled speech enhancement and recognition in noisy, reverberant environments by combining single-channel LSTM speech enhancement with multi-channel dereverberation and LSTM language models, achieving a Word Error Rate of 24.38%, a 25% relative improvement over the baseline.
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.