OBJECTIVE COMPARISON OF SPEECH ENHANCEMENT ALGORITHMS WITH HEARING LOSS SIMULATION

Many speech enhancement algorithms have been proposed over the years and it has been shown that deep neural networks can lead to significant improvements. These algorithms, however, have not been validated for hearing-impaired listeners. Additionally, these algorithms are often evaluated under a limited range of signal-to-noise ratios (SNR). Here, we construct a diverse speech dataset with a broad range of SNRs and noises. Several enhancement algorithms are compared under both normal-hearing and simulated hearing-impaired conditions, where the perceptual evaluation of speech quality (PESQ) and hearing-aid speech quality index (HASQI) are used as objective metrics. The impact of the data's frequency scale (Mel versus linear) on performance is also evaluated. Results show that a long short-term memory (LSTM) network with data in the Mel-frequency domain yields the best performance for PESQ, and a Bidirectional LSTM network with data in the linear frequency scale performs the best in hearing-impaired settings. The Mel-frequency scale results in improved PESQ scores, but reduced HASQI scores.