Determination of the Potential Benefit of Time-Frequency Gain Manipulation

Anzalone, Michael C.; Calandruccio, Lauren; Doherty, Karen A.; Carney, Laurel H.

doi:10.1097/01.aud.0000233891.86809.df

Cited by 93 publications

(99 citation statements)

References 44 publications

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“…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.…”

Section: Hi-listener and Babble Advantagessupporting

confidence: 75%

“…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.…”

Section: Algorithm and Ibm Processingsupporting

confidence: 73%

“…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).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Healy

Yoho

Wang

et al. 2014

The Journal of the Acoustical Society of America

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Consonant recognition was assessed following extraction of speech from noise using a more efficient version of the speech-segregation algorithm described in Healy, Yoho, Wang, and Wang [(2013) J. Acoust. Soc. Am. 134, 3029-3038]. Substantial increases in recognition were observed following algorithm processing, which were significantly larger for hearing-impaired (HI) than for normalhearing (NH) listeners in both speech-shaped noise and babble backgrounds. As observed previously for sentence recognition, older HI listeners having access to the algorithm performed as well or better than young NH listeners in conditions of identical noise. It was also found that the binary masks estimated by the algorithm transmitted speech features to listeners in a fashion highly similar to that of the ideal binary mask (IBM), suggesting that the algorithm is estimating the IBM with substantial accuracy. Further, the speech features associated with voicing, manner of articulation, and place of articulation were all transmitted with relative uniformity and at relatively high levels, indicating that the algorithm and the IBM transmit speech cues without obvious deficiency. Because the current implementation of the algorithm is much more efficient, it should be more amenable to real-time implementation in devices such as hearing aids and cochlear implants.

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Section: Hi-listener and Babble Advantagessupporting

confidence: 75%

Section: Algorithm and Ibm Processingsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Healy

Yoho

Wang

et al. 2014

The Journal of the Acoustical Society of America

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show abstract

“…A number of other studies (Roman et al, 2003;Roman and Wang, 2006;Cooke, 2006;Brungart et al, 2006;Anzalone et al, 2006) have shown that speech synthesized from the ideal binary mask is highly intelligible even when extracted from multi-source mixtures (Roman et al, 2003) or in reverberant conditions (Roman and Wang, 2006). Ideal masks that are not binary but take real values have also been found to improve speech intelligibility (Li and Loizou, 2007).…”

Section: Introductionmentioning

confidence: 99%

Factors influencing intelligibility of ideal binary-masked speech: Implications for noise reduction

Loizou

2008

The Journal of the Acoustical Society of America

221

196

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The application of the ideal binary mask to an auditory mixture has been shown to yield substantial improvements in intelligibility. This mask is commonly applied to the time-frequency (T-F) representation of a mixture signal and eliminates portions of a signal below a signal-to-noise-ratio (SNR) threshold while allowing others to pass through intact. The factors influencing intelligibility of ideal binary-masked speech are not well understood and are examined in the present study. Specifically, the effects of the local SNR threshold, input SNR level, masker type and errors introduced in estimating the ideal mask are examined. Consistent with previous studies, intelligibility of binary-masked stimuli is quite high even at -10 dB SNR for all maskers tested. Performance was affected the most when the masker dominated T-F units were wrongly labeled as target-dominated T-F units. Performance plateaued near 100% correct for SNR thresholds ranging from -20 dB to 5 dB. We believe the existence of the plateau region suggests that it is the pattern of the ideal binary mask that matters the most rather than the local SNR of each T-F unit. This pattern directs the listener's attention to where the target is and enables them to segregate speech effectively in multi-talker environments.

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“…Specifically, for a time-frequency (T-F) unit, if the signalto-noise ratio (SNR) within the unit is greater than a local criterion (LC), it will be labeled as 1 and otherwise it will be labeled as 0. Previous studies show that IBM separation produces large improvements in human speech intelligibility [3,4,5]. Therefore, one way to approach monaural speech separation is to estimate the IBM.…”

Section: Introductionmentioning

confidence: 99%

An SVM based classification approach to speech separation

Han

Wang

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Monaural speech separation is a very challenging task. CASAbased systems utilize acoustic features to produce a time-frequency (T-F) mask. In this study, we propose a classification approach to monaural separation problem. Our feature set consists of pitchbased features and amplitude modulation spectrum features, which can discriminate both voiced and unvoiced speech from nonspeech interference. We employ support vector machines (SVMs) followed by a re-thresholding method to classify each T-F unit as either targetdominated or interference-dominated. An auditory segmentation stage is then utilized to improve SVM-generated results. Systematic evaluations show that our approach produces high quality binary masks and outperforms a previous system in terms of classification accuracy.

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Determination of the Potential Benefit of Time-Frequency Gain Manipulation

Cited by 93 publications

References 44 publications

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

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

Factors influencing intelligibility of ideal binary-masked speech: Implications for noise reduction

An SVM based classification approach to speech separation

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