An evaluation of intrusive instrumental intelligibility metrics

Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and $\text{sEPSM}^\text{corr}$. In addition, this paper investigates the ability of intelligibility metrics to generalize to new types of distortions and analyzes why the top performing metrics have high performance. The intelligibility data were obtained from 11 listening tests described in the literature. The stimuli included Dutch, Danish, and English speech that was distorted by additive noise, reverberation, competing talkers, pre-processing enhancement, and post-processing enhancement. SIIB and HASPI had the highest performance achieving a correlation with listening test scores on average of $\rho=0.92$ and $\rho=0.89$, respectively. The high performance of SIIB may, in part, be the result of SIIBs developers having access to all the intelligibility data considered in the evaluation. The results show that intelligibility metrics tend to perform poorly on data sets that were not used during their development. By modifying the original implementations of SIIB and STOI, the advantage of reducing statistical dependencies between input features is demonstrated. Additionally, the paper presents a new version of SIIB called $\text{SIIB}^\text{Gauss}$, which has similar performance to SIIB and HASPI, but takes less time to compute by two orders of magnitude.Comment: Published in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 201

A Weighted STOI Intelligibility Metric Based On Mutual Information

It is known that the information required for the intelligibility of a speech signal is distributed non-uniformly in time. In this paper we propose WSTOI, a modified version of STOI, a speech intelligibility metric. With WSTOI the contribution of each time-frequency cell is weighted by an estimate of its intelligibility content. This estimate is equal to the mutual information between two hypothetical signals at either end of a simplified model of human communication. Listening tests show that the modification improves the prediction accuracy of STOI at all performance levels on both long and short utterances. An improvement was observed across all tested noise types and suppression algorithms

Evaluation of the Importance of Time-Frequency Contributions to Speech Intelligibility in Noise

Recent studies on binary masking techniques make the assumption that each time-frequency (T-F) unit contributes an equal amount to the overall intelligibility of speech. The present study demonstrated that the importance of each T-F unit to speech intelligibility varies in accordance with speech content. Specifically, T-F units are categorized into two classes, speech-present T-F units and speech-absent T-F units. Results indicate that the importance of each speech-present T-F unit to speech intelligibility is highly related to the loudness of its target component, while the importance of each speech-absent T-F unit varies according to the loudness of its masker component. Two types of mask errors are also considered, which include miss and false alarm errors. Consistent with previous work, false alarm errors are shown to be more harmful to speech intelligibility than miss errors when the mixture signal-to-noise ratio (SNR) is below 0 dB. However, the relative importance between the two types of error is conditioned on the SNR level of the input speech signal. Based on these observations, a mask-based objective measure, the loudness weighted hit-false, is proposed for predicting speech intelligibility. The proposed objective measure shows significantly higher correlation with intelligibility compared to two existing mask-based objective measures