Speech intelligibility in background noise with ideal binary time-frequency masking

Abstract

Ideal binary time-frequency masking is a signal separation technique that retains mixture energy in time-frequency units where local signal-to-noise ratio exceeds a certain threshold and rejects mixture energy in other time-frequency units. Two experiments were designed to assess the effects of ideal binary masking on speech intelligibility of both normal-hearing (NH) and hearing-impaired (HI) listeners in different kinds of background interference. The results from Experiment 1 demonstrate that ideal binary masking leads to substantial reductions in speech-reception threshold for both NH and HI listeners, and the reduction is greater in a cafeteria background than in a speech-shaped noise. Furthermore, listeners with hearing loss benefit more than listeners with normal hearing, particularly for cafeteria noise, and ideal masking nearly equalizes the speech intelligibility performances of NH and HI listeners in noisy backgrounds. The results from Experiment 2 suggest that ideal binary masking in the low-frequency range yields larger intelligibility improvements than in the high-frequency range, especially for listeners with hearing loss. The findings from the two experiments have major implications for understanding speech perception in noise, computational auditory scene analysis, speech enhancement, and hearing aid design.

Speech-cue transmission by an algorithm to increase consonant recognition in noise for hearing-impaired listeners

“…In accord with previous work involving sentences and either algorithm processing (Healy et al, 2013) or IBM processing (Anzalone et al, 2006;Wang et al, 2009), gains were greater for HI than for NH listeners and for modulated than for stationary backgrounds. The HI-listener advantage is promising because it indicates that the algorithm and the IBM are effective despite the multitude of limitations imposed by the impaired auditory system.…”

“…As with the algorithm, it was found that the IBM improved recognition of isolated phonemes, thus extending results observed previously for sentences (Anzalone et al, 2006;Brungart et al, 2006;Li and Loizou, 2008;Wang et al, 2008Wang et al, , 2009Cao et al, 2011;Sinex, 2013). This result should not be surprising, given the effectiveness of algorithm processing observed here and the fact that the algorithm aimed to estimate the IBM.…”

“…This difference is generally obscured when using sentence materials, as IBM scores tend to reach ceiling values (e.g., Brungart et al, 2006;Li and Loizou, 2008;Wang et al, 2009;Sinex, 2013). Another consequence is that the increased effectiveness of IBM processing for HI listeners is reflected with clarity when assessed as the distance between IBM scores and scores in quiet (see Fig.…”

“…Ideal binary-mask processing followed closely that of Wang et al (2009). Specifically, the signals were divided using a 64-channel gammatone filterbank having center frequencies ranging from 50 to 8000 Hz, equally spaced in equivalent rectangular bandwidths (Glasberg and Moore, 1990).…”

“…In the Ideal Binary Mask (IBM), the premixed speech and noise signals are known, and the mask corresponds to ideal classification. IBM processing produces remarkable speech-intelligibility improvements in noise for both HI and NH listeners, even at extremely low SNRs (Anzalone et al, 2006;Brungart et al, 2006;Li and Loizou, 2008;Wang et al, 2008Wang et al, , 2009Cao et al, 2011;Sinex, 2013).…”