Evaluating a distortion-weighted glimpsing metric for predicting binaural speech intelligibility in rooms

A distortion-weighted glimpse proportion metric (BiDWGP) for predicting binaural speech intelligibility were evaluated in simulated anechoic and reverberant conditions, with and without a noise masker. The predictive performance of BiDWGP was compared to four reference binaural intelligibility metrics, which were extended from the Speech Intelligibility Index (SII) and the Speech Transmission Index (STI). In the anechoic sound field, BiDWGP demonstrated high accuracy in predicting binaural intelligibility for individual maskers (ρ ≥ 0.95) and across maskers (ρ ≥ 0.94). The reference metrics however performed less well in across-masker prediction (0.54 ≤ ρ ≤ 0.86) despite reasonable accuracy for individual maskers. In reverberant rooms, BiDWGP was more stable in all test conditions (ρ ≥ 0.87) than the reference metrics, which showed different predictive patterns: the binaural STIs were more robust for the stationary than for the fluctuating noise masker, whilst the binaural SII displayed the opposite behaviour. The study shows that the new BiDWGP metric can provide similar or even more robust predictive power than the current standard metric

A metric for predicting binaural speech intelligibility in stationary noise and competing speech maskers

One criterion in the design of binaural sound scenes in audio production is the extent to which the intended speech message is correctly understood. Object-based audio broadcasting systems have permitted sound editors to gain more access to the metadata (e.g., intensity and location) of each sound source, providing better control over speech intelligibility. The current study describes and evaluates a binaural distortion-weighted glimpse proportion metric -- BiDWGP -- which is motivated by better-ear glimpsing and binaural masking level differences. BiDWGP predicts intelligibility from two alternative input forms: either binaural recordings or monophonic recordings from each sound source along with their locations. Two listening experiments were performed with stationary noise and competing speech, one in the presence of a single masker, the other with multiple maskers, for a variety of spatial configurations. Overall, BiDWGP with both input forms predicts listener keyword scores with correlations of 0.95 and 0.91 for single- and multi-masker conditions, respectively. When considering masker type separately, correlations rise to 0.95 and above for both types of maskers. Predictions using the two input forms are very similar, suggesting that BiDWGP can be applied to the design of sound scenes where only individual sound sources and their locations are available

A non-intrusive method for estimating binaural speech intelligibility from noise-corrupted signals captured by a pair of microphones

A non-intrusive method is introduced to predict binaural speech intelligibility in noise directly from signals captured using a pair of microphones. The approach combines signal processing techniques in blind source separation and localisation, with an intrusive objective intelligibility measure (OIM). Therefore, unlike classic intrusive OIMs, this method does not require a clean reference speech signal and knowing the location of the sources to operate. The proposed approach is able to estimate intelligibility in stationary and fluctuating noises, when the noise masker is presented as a point or diffused source, and is spatially separated from the target speech source on a horizontal plane. The performance of the proposed method was evaluated in two rooms. When predicting subjective intelligibility measured as word recognition rate, this method showed reasonable predictive accuracy with correlation coefficients above 0.82, which is comparable to that of a reference intrusive OIM in most of the conditions. The proposed approach offers a solution for fast binaural intelligibility prediction, and therefore has practical potential to be deployed in situations where on-site speech intelligibility is a concern

Acoustic source separation based on target equalization-cancellation

Normal-hearing listeners are good at focusing on the target talker while ignoring the interferers in a multi-talker environment. Therefore, efforts have been devoted to build psychoacoustic models to understand binaural processing in multi-talker environments and to develop bio-inspired source separation algorithms for hearing-assistive devices. This thesis presents a target-Equalization-Cancellation (target-EC) approach to the source separation problem. The idea of the target-EC approach is to use the energy change before and after cancelling the target to estimate a time-frequency (T-F) mask in which each entry estimates the strength of target signal in the original mixture. Once the mask is calculated, it is applied to the original mixture to preserve the target-dominant T-F units and to suppress the interferer-dominant T-F units. On the psychoacoustic modeling side, when the output of the target-EC approach is evaluated with the Coherence-based Speech Intelligibility Index (CSII), the predicted binaural advantage closely matches the pattern of the measured data. On the application side, the performance of the target-EC source separation algorithm was evaluated by psychoacoustic measurements using both a closed-set speech corpus and an open-set speech corpus, and it was shown that the target-EC cue is a better cue for source separation than the interaural difference cues

Intelligibility prediction for speech mixed with white Gaussian noise at low signal-to-noise ratios

The effect of additive white Gaussian noise and high-pass filtering on speech intelligibility at signal-to-noise ratios (SNRs) from -26 to 0 dB was evaluated using British English talkers and normal hearing listeners. SNRs below -10 dB were considered as they are relevant to speech security applications. Eight objective metrics were assessed: Short-Time Objective Intelligibility (STOI), a proposed variant termed STOI+, Extended Short-Time Objective Intelligibility (ESTOI), Normalised Covariance Metric (NCM), Normalised Sub-band Envelope Correlation metric (NSEC), two metrics derived from the Coherence Speech Intelligibility Index (CSII), and an envelope-based regression method Speech Transmission Index (STI). For speech and noise mixtures associated with intelligibility scores ranging from 0% to 98%, STOI+ performed at least as well as other metrics, and under some conditions better than STOI, ESTOI, STI, NSEC, CSIIMid and CSIIHigh. Both STOI+ and NCM were associated with relatively low prediction error and bias for intelligibility prediction at SNRs from -26 to 0 dB. STI performed least well in terms of correlation with intelligibility scores, prediction error, bias and reliability. Logistic regression modelling demonstrated that high-pass filtering, which increases the proportion of high to low frequency energy, was detrimental to intelligibility for SNRs between -5 and -17 dB inclusive