P. V. Muhammed Shifas

Muhammed PV Shifas, Anna Sfakianaki, Theognosia Chimona et al.

In this work we evaluate a neural based speech intelligibility booster based on spectral shaping and dynamic range compression (SSDRC), referred to as WaveNet-based SSDRC (wSSDRC), using a recently designed Greek Harvard-style corpus. The corpus has been developed according to the format of the Harvard/IEEE sentences and offers the opportunity to apply neural speech enhancement models and examine their performance gain for Greek listeners. wSSDRC has been successfully tested for English material and speakers in the past. In this paper we revisit wSSDRC to perform a full scale evaluation of the model with Greek listeners under the condition of equal energy before and after modification. Both normal hearing (NH) and hearing impaired (HI) listeners evaluated the model under speech shaped noise (SSN) at listener-specific SNRs matching their Speech Reception Threshold (SRT) - a point at which 50 % of unmodified speech is intelligible. The analysis statistics show that the wSSDRC model has produced a median intelligibility boost of 39% for NH and 38% for HI, relative to the plain unprocessed speech.

Muhammed PV Shifas, Santelli Claudio, Vassilis Tsiaras et al.

Convolutional neural network (CNN) modules are widely being used to build high-end speech enhancement neural models. However, the feature extraction power of vanilla CNN modules has been limited by the dimensionality constraint of the convolution kernels that are integrated - thereby, they have limitations to adequately model the noise context information at the feature extraction stage. To this end, adding recurrency factor into the feature extracting CNN layers, we introduce a robust context-aware feature extraction strategy for single-channel speech enhancement. As shown, adding recurrency results in capturing the local statistics of noise attributes at the extracted features level and thus, the suggested model is effective in differentiating speech cues even at very noisy conditions. When evaluated against enhancement models using vanilla CNN modules, in unseen noise conditions, the suggested model with recurrency in the feature extraction layers has produced a segmental SNR (SSNR) gain of up to 1.5 dB, an improvement of 0.4 in subjective quality in the Mean Opinion Score scale, while the parameters to be optimized are reduced by 25%.