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IntroductionWe examined which brain areas are involved in the comprehension of acoustically distorted speech using an experimental paradigm where the same distorted sentence can be perceived at different levels of intelligibility. This change in intelligibility occurs via a single intervening presentation of the intact version of the sentence, and the effect lasts at least on the order of minutes. Since the acoustic structure of the distorted stimulus is kept fixed and only intelligibility is varied, this allows one to study brain activity related to speech comprehension specifically.MethodsIn a functional magnetic resonance imaging (fMRI) experiment, a stimulus set contained a block of six distorted sentences. This was followed by the intact counterparts of the sentences, after which the sentences were presented in distorted form again. A total of 18 such sets were presented to 20 human subjects.ResultsThe blood oxygenation level dependent (BOLD)‐responses elicited by the distorted sentences which came after the disambiguating, intact sentences were contrasted with the responses to the sentences presented before disambiguation. This revealed increased activity in the bilateral frontal pole, the dorsal anterior cingulate/paracingulate cortex, and the right frontal operculum. Decreased BOLD responses were observed in the posterior insula, Heschl's gyrus, and the posterior superior temporal sulcus.ConclusionsThe brain areas that showed BOLD‐enhancement for increased sentence comprehension have been associated with executive functions and with the mapping of incoming sensory information to representations stored in episodic memory. Thus, the comprehension of acoustically distorted speech may be associated with the engagement of memory‐related subsystems. Further, activity in the primary auditory cortex was modulated by prior experience, possibly in a predictive coding framework. Our results suggest that memory biases the perception of ambiguous sensory information toward interpretations that have the highest probability to be correct based on previous experience.

In shouting, speakers use increased vocal effort to convey spoken messages over distance or above environmental noise. For automatic speaker recognition systems trained using normal speech, shouting causes a severe vocal effort mismatch between the enrollment and test hence reducing the recognition performance. In this study, two compensation methods are proposed to tackle the mismatch in a shouted versus normal speaker recognition task. These techniques are applied in the feature extraction stage of a speaker recognition system to modify the spectral envelopes of shouts to be closer to those in normal speech. The techniques modify the all-pole power spectrum of the MFCC computation chain with shouted-to-normal compensation filtering that is obtained using a GMM-based statistical mapping. In an evaluation using the state-of-the-art i-vector based recognition system, the proposed techniques provided considerable improvements in identification rates compared to the case when shouted speech spectra were not processed.

Post-filtering can be utilized to improve the quality and intelligibility of telephone speech. Previous studies have shown that energy reallocation with a high-pass type filter works effectively in improving the intelligibility of speech in difficult noise conditions. The present study introduces a signal-to-noise ratio adaptive post-filtering method that utilizes energy reallocation to transfer energy from the first formant to higher frequencies. The proposed method adapts to the level of the background noise so that, in favorable noise conditions, the post-filter has a flat frequency response and the effect of the post-filtering is increased as the level of the ambient noise increases. The performance of the proposed method is compared with a similar post-filtering algorithm and unprocessed speech in subjective listening tests which evaluate both intelligibility and listener preference. The results indicate that both of the post-filtering methods maintain the quality of speech in negligible noise conditions and are able to provide intelligibility improvement over unprocessed speech in adverse noise conditions. Furthermore, the proposed post-filtering algorithm performs better than the other post-filtering method under evaluation in moderate to difficult noise conditions, where intelligibility improvement is mostly required.

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