Lexical Effects on the Perceived Clarity of Noise-Vocoded Speech in Younger and Older Listeners.

When listening to degraded speech, such as speech delivered by a cochlear implant (CI), listeners make use of top-down linguistic knowledge to facilitate speech recognition. Lexical knowledge supports speech recognition and enhances the perceived clarity of speech. Yet, the extent to which lexical knowledge can be used to effectively compensate for degraded input may depend on the degree of degradation and the listener's age. The current study investigated lexical effects in the compensation for speech that was degraded via noise-vocoding in younger and older listeners. In an online experiment, younger and older normal-hearing (NH) listeners rated the clarity of noise-vocoded sentences on a scale from 1 ("very unclear") to 7 ("completely clear"). Lexical information was provided by matching text primes and the lexical content of the target utterance. Half of the sentences were preceded by a matching text prime, while half were preceded by a non-matching prime. Each sentence also consisted of three key words of high or low lexical frequency and neighborhood density. Sentences were processed to simulate CI hearing, using an eight-channel noise vocoder with varying filter slopes. Results showed that lexical information impacted the perceived clarity of noise-vocoded speech. Noise-vocoded speech was perceived as clearer when preceded by a matching prime, and when sentences included key words with high lexical frequency and low neighborhood density. However, the strength of the lexical effects depended on the level of degradation. Matching text primes had a greater impact for speech with poorer spectral resolution, but lexical content had a smaller impact for speech with poorer spectral resolution. Finally, lexical information appeared to benefit both younger and older listeners. Findings demonstrate that lexical knowledge can be employed by younger and older listeners in cognitive compensation during the processing of noise-vocoded speech. However, lexical content may not be as reliable when the signal is highly degraded. Clinical implications are that for adult CI users, lexical knowledge might be used to compensate for the degraded speech signal, regardless of age, but some CI users may be hindered by a relatively poor signal.

Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed to assess the ability to generalize across extremely different training versus test environments. Training and testing were performed using different languages having no known common ancestry and correspondingly large linguistic differences-English for training and Mandarin for testing. Additional generalizations included untrained speech corpus/recording channel, target-to-interferer energy ratios, reverberation room impulse responses, and test talkers. A deep computational auditory scene analysis algorithm, employing complex time-frequency masking to estimate both magnitude and phase, was used to segregate two concurrent talkers and simultaneously remove large amounts of room reverberation to increase the intelligibility of a target talker. Significant intelligibility improvements were observed for the normal-hearing listeners in every condition. Benefit averaged 43.5% points across conditions and was comparable to that obtained when training and testing were performed both in English. Benefit is projected to be considerably larger for individuals with hearing impairment. It is concluded that a properly designed and trained deep speaker separation/dereverberation network can be capable of generalization across vastly different acoustic environments that include different languages.

Forward Digit Span and Word Familiarity Do Not Correlate With Differences in Speech Recognition in Individuals With Cochlear Implants After Accounting for Auditory Resolution.

Purpose In individuals with cochlear implants, speech recognition is not associated with tests of working memory that primarily reflect storage, such as forward digit span. In contrast, our previous work found that vocoded speech recognition in individuals with normal hearing was correlated with performance on a forward digit span task. A possible explanation for this difference across groups is that variability in auditory resolution across individuals with cochlear implants could conceal the true relationship between speech and memory tasks. Here, our goal was to determine if performance on forward digit span and speech recognition tasks are correlated in individuals with cochlear implants after controlling for individual differences in auditory resolution. Method We measured sentence recognition ability in 20 individuals with cochlear implants with Perceptually Robust English Sentence Test Open-set sentences. Spectral and temporal modulation detection tasks were used to assess individual differences in auditory resolution, auditory forward digit span was used to assess working memory storage, and self-reported word familiarity was used to assess vocabulary. Results Individual differences in speech recognition were predicted by spectral and temporal resolution. A correlation was found between forward digit span and speech recognition, but this correlation was not significant after controlling for spectral and temporal resolution. No relationship was found between word familiarity and speech recognition. Forward digit span performance was not associated with individual differences in auditory resolution. Conclusions Our findings support the idea that sentence recognition in individuals with cochlear implants is primarily limited by individual differences in working memory processing, not storage. Studies examining the relationship between speech and memory should control for individual differences in auditory resolution.

Spectro-temporal glimpsing of speech in noise: Regularity and coherence of masking patterns reduces uncertainty and increases intelligibility.

Adverse listening conditions involve glimpses of spectro-temporal speech information. This study investigated if the acoustic organization of the spectro-temporal masking pattern affects speech glimpsing in "checkerboard" noise. The regularity and coherence of the masking pattern was varied. Regularity was reduced by randomizing the spectral or temporal gating of the masking noise. Coherence involved the spectral alignment of frequency bands across time or the temporal alignment of gated onsets/offsets across frequency bands. Experiment 1 investigated the effect of spectral or temporal coherence. Experiment 2 investigated independent and combined factors of regularity and coherence. Performance was best in spectro-temporally modulated noise having larger glimpses. Generally, performance also improved as the regularity and coherence of masker fluctuations increased, with regularity having a stronger effect than coherence. An acoustic glimpsing model suggested that the effect of regularity (but not coherence) could be partially attributed to the availability of glimpses retained after energetic masking. Performance tended to be better with maskers that were spectrally coherent as compared to temporally coherent. Overall, performance was best when the spectro-temporal masking pattern imposed even spectral sampling and minimal temporal uncertainty, indicating that listeners use reliable masking patterns to aid in spectro-temporal speech glimpsing.