For a long time, the compensation effect of hearing aid is mainly evaluated subjectively, and there are fewer studies of objective evaluation. Furthermore, a pure speech signal is generally required as a reference in the existing objective evaluation methods, which restricts the practicality in a real-world environment. Therefore, this paper presents a non-intrusive speech quality evaluation method for hearing aid, which combines the audiogram and weighted frequency information. The proposed model mainly includes an audiogram information extraction network, a frequency information extraction network, and a quality score mapping network. The audiogram is the input of the audiogram information extraction network, which helps the system capture the information related to hearing loss. In addition, the low-frequency bands of speech contain loudness information and the medium and high-frequency components contribute to semantic comprehension. The information of two frequency bands is input to the frequency information extraction network to obtain time-frequency information. When obtaining the high-level features of different frequency bands and audiograms, they are fused into two groups of tensors that distinguish the information of different frequency bands and used as the input of the attention layer to calculate the corresponding weight distribution. Finally, a dense layer is employed to predict the score of speech quality. The experimental results show that it is reasonable to combine the audiogram and the weight of the information from two frequency bands, which can effectively realize the evaluation of the speech quality of the hearing aid.

We proposed and evaluated a speech packet loss concealment method which predicts lost segments from speech included in packets either before, or both before and after the lost packet. The lost segments are predicted recursively by using linear prediction both in the forward direction from the packet preceding the loss, and in the backward direction from the packet succeeding the lost segment. Predicted samples in each direction are smoothed by averaging using linear weights to obtain the final interpolated signal. The adjacent segments are also smoothed extensively to significantly reduce the speech quality discontinuity between the interpolated signal and the received speech signal. Subjective quality comparisons between the proposed method and the the packet loss concealment algorithm described in the ITU standard G.711 Appendix I showed similar scores up to about 10% packet loss. However, the proposed method showed higher scores above this loss rate, with Mean Opinion Score rating exceeding 2.4, even at an extremely high packet loss rate of 30%. Packet loss concealment of speech degraded with G.729 coding, and babble noise mixed speech showed similar trends, with the proposed method showing higher qualities at high loss rates. We plan to further improve the performance by using adaptive LPC prediction order depending on the estimated pitch, and adaptive LPC bandwidth expansion depending on the consecutive number of repetitive prediction, among many other improvements. We also plan to investigate complexity reduction using gradient LPC coefficient updates, and processing delay reduction using adaptive forward/bidirectional prediction modes depending on the measured packet loss ratio.