基于听觉机制的语音可懂度预测模型，更准确评估老年人语音增强效果。

We propose an objective intelligibility measure (OIM), called the Gammachirp Envelope Similarity Index (GESI), that can predict speech intelligibility (SI) in older adults. GESI is a bottom-up model based on psychoacoustic knowledge from the peripheral to the central auditory system. It computes the single SI metric using the gammachirp filterbank (GCFB), the modulation filterbank, and the extended cosine similarity measure. It takes into account not only the hearing level represented in the audiogram, but also the temporal processing characteristics captured by the temporal modulation transfer function (TMTF). To evaluate performance, SI experiments were conducted with older adults of various hearing levels using speech-in-noise with ideal speech enhancement on familiarity-controlled Japanese words. The prediction performance was compared with HASPIw2, which was developed for keyword SI prediction. The results showed that GESI predicted the subjective SI scores more accurately than HASPIw2. GESI was also found to be at least as effective as, if not more effective than, HASPIv2 in predicting English sentence-level SI. The effect of introducing TMTF into the GESI algorithm was insignificant, suggesting that TMTF measurements and models are not yet mature. Therefore, it may be necessary to perform TMTF measurements with bandpass noise and to improve the incorporation of temporal characteristics into the model.