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In recent years, objective measures of speech intelligibility have gained increasing interest. However, most speech intelligibility metrics require a clean reference signal, which is often not available in real-life applications. In a recent publication, we proposed a method, the Non-Intrusive Codebook-based ShortTime Objective Intelligibility (NIC-STOI) metric, which allows using an intrusive method without requiring access to the clean signal. The statistics of the reference signal is estimated as a combination of predefined codebooks that best fit the degraded signal by modeling the speech and noisy spectra. In this paper, we perform additional validation of the NIC-STOI in more diverse noise condition as well as for speech processed nonlinearly with binary masks, where it is shown to outperform existing non-intrusive metrics.

Validation of the Non-Intrusive Codebook-Based Short Time Objective Intelligibility Metric for Processed Speech