Harmonic Beamformers for Non-Intrusive Speech Intelligibility Prediction

In recent years, research into objective speech intelligibil- ity measures has gained increased interest as a tool to opti- mize speech enhancement algorithms. While most intelligi- bility measures are intrusive, i.e., they require a clean refer- ence signal, this is rarely available in real-time applications. This paper proposes two non-intrusive intelligibility measures, which allow using the intrusive short-time objective intelligibil- ity (STOI) measure without requiring access to the clean signal. Instead, a reference signal is obtained from the degraded sig- nal using either a fixed or an adaptive harmonic spatial filter. This reference signal is then used as input to STOI. The exper- imental results show a high correlation between both proposed non-intrusive speech intelligibility measures and the original in- trusively computed STOI scores.