Acoustics and Perception of Clear Fricatives

Everyday observation indicates that speakers can naturally and spontaneously adopt a speaking style that allows them to be understood more easily when confronted with difficult communicative situations. Previous studies have demonstrated that the resulting speaking style, known as clear speech, is more intelligible than casual, conversational speech for a variety of listener populations. However, few studies have examined the acoustic properties of clearly produced fricatives in detail. In addition, it is unknown whether clear speech improves the intelligibility of fricative consonants, or how its effects on fricative perception might differ depending on listener population. Since fricatives are the cause of a large number of recognition errors both for normal-hearing listeners in adverse conditions and for hearing-impaired listeners, it is of interest to explore these issues in detail focusing on fricatives. The current study attempts to characterize the type and magnitude of adaptations in the clear production of English fricatives and determine whether clear speech enhances fricative intelligibility for normal-hearing listeners and listeners with simulated impairment. In an acoustic experiment (Experiment I), ten female and ten male talkers produced nonsense syllables containing the fricatives /f, &thetas;, s, [special characters omitted], v, δ, z, and [y]/ in VCV contexts, in both a conversational style and a clear style that was elicited by means of simulated recognition errors in feedback received from an interactive computer program. Acoustic measurements were taken for spectral, amplitudinal, and temporal properties known to influence fricative recognition. Results illustrate that (1) there were consistent overall clear speech effects, several of which (consonant duration, spectral peak location, spectral moments) were consistent with previous findings and a few (notably consonant-to-vowel intensity ratio) which were not, (2) 'contrastive' differences related to acoustic inventory and eliciting prompts were observed in key comparisons, and (3) talkers differed widely in the types and magnitude of acoustic modifications. Two perception experiments using these same productions as stimuli (Experiments II and III) were conducted to address three major questions: (1) whether clearly produced fricatives are more intelligible than conversational fricatives, (2) what specific acoustic modifications are related to clear speech intelligibility advantages, and (3) how sloping, recruiting hearing impairment interacts with clear speech strategies. Both perception experiments used an adaptive procedure to estimate the signal to (multi-talker babble) noise ratio (SNR) threshold at which minimal pair fricative categorizations could be made with 75% accuracy. Data from fourteen normal-hearing listeners (Experiment II) and fourteen listeners with simulated sloping elevated thresholds and loudness recruitment (Experiment III) indicate that clear fricatives were more intelligible overall for both listener groups. However, for listeners with simulated hearing impairment, a reliable clear speech intelligibility advantage was not found for non-sibilant pairs. Correlation analyses comparing acoustic and perceptual style-related differences across the 20 speakers encountered in the experiments indicated that a shift of energy concentration toward higher frequency regions and greater source strength was a primary contributor to the "clear fricative effect" for normal-hearing listeners but not for listeners with simulated loss, for whom information in higher frequency regions was less audible

Features of hearing: applications of machine learning to uncover the building blocks of hearing

Recent advances in machine learning have instigated a renewed interest in using machine learning approaches to better understand human sensory processing. This line of research is particularly interesting for speech research since speech comprehension is uniquely human, which complicates obtaining detailed neural recordings. In this thesis, I explore how machine learning can be used to uncover new knowledge about the auditory system, with a focus on discovering robust auditory features. The resulting increased understanding of the noise robustness of human hearing may help to better assist those with hearing loss and improve Automatic Speech Recognition (ASR) systems. First, I show how computational neuroscience and machine learning can be combined to generate hypotheses about auditory features. I introduce a neural feature detection model with a modest number of parameters that is compatible with auditory physiology. By testing feature detector variants in a speech classification task, I confirm the importance of both well-studied and lesser-known auditory features. Second, I investigate whether ASR software is a good candidate model of the human auditory system. By comparing several state-of-the-art ASR systems to the results from humans on a range of psychometric experiments, I show that these ASR systems diverge markedly from humans in at least some psychometric tests. This implies that none of these systems act as a strong proxy for human speech recognition, although some may be useful when asking more narrowly defined questions. For neuroscientists, this thesis exemplifies how machine learning can be used to generate new hypotheses about human hearing, while also highlighting the caveats of investigating systems that may work fundamentally differently from the human brain. For machine learning engineers, I point to tangible directions for improving ASR systems. To motivate the continued cross-fertilization between these fields, a toolbox that allows researchers to assess new ASR systems has been released.Open Acces

Ultra-high-speed imaging of bubbles interacting with cells and tissue

Ultrasound contrast microbubbles are exploited in molecular imaging, where bubbles are directed to target cells and where their high-scattering cross section to ultrasound allows for the detection of pathologies at a molecular level. In therapeutic applications vibrating bubbles close to cells may alter the permeability of cell membranes, and these systems are therefore highly interesting for drug and gene delivery applications using ultrasound. In a more extreme regime bubbles are driven through shock waves to sonoporate or kill cells through intense stresses or jets following inertial bubble collapse. Here, we elucidate some of the underlying mechanisms using the 25-Mfps camera Brandaris128, resolving the bubble dynamics and its interactions with cells. We quantify acoustic microstreaming around oscillating bubbles close to rigid walls and evaluate the shear stresses on nonadherent cells. In a study on the fluid dynamical interaction of cavitation bubbles with adherent cells, we find that the nonspherical collapse of bubbles is responsible for cell detachment. We also visualized the dynamics of vibrating microbubbles in contact with endothelial cells followed by fluorescent imaging of the transport of propidium iodide, used as a membrane integrity probe, into these cells showing a direct correlation between cell deformation and cell membrane permeability

Why reduce? phonological neighborhood density and phonetic reduction in spontaneous speech

2011-2012 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A Novel Non-Acoustic Voiced Speech Sensor: Experimental Results and Characterization

Recovering clean speech from an audio signal with additive noise is a problem that has plagued the signal processing community for decades. One promising technique currently being utilized in speech-coding applications is a multi-sensor approach, in which a microphone is used in conjunction with optical, mechanical, and electrical non-acoustic speech sensors to provide greater versatility in signal processing algorithms. One such non-acoustic glottal waveform sensor is the Tuned Electromagnetic Resonator Collar (TERC) sensor, first developed in [BLP+02]. The sensor is based on Magnetic Resonance Imaging (MRI) concepts, and is designed to detect small changes in capacitance caused by changes to the state of the vocal cords - the glottal waveform. Although preliminary simulations in [BLP+02] have validated the basic theory governing the TERC sensor\u27s operation, results from human subject testing are necessary to accurately characterize the sensor\u27s performance in practice. To this end, a system was designed and developed to provide real-time audio recordings from the sensor while attached to a human test subject. From these recordings, executed in a variety of acoustic noise environments, the practical functionality of the TERC sensor was demonstrated. The sensor in its current evolution is able to detect a periodic waveform during voiced speech, with two clear harmonics and a fundamental frequency equal to that of the speech it is detecting. This waveform is representative of the glottal waveform, with little or no articulation as initially hypothesized. Though statistically significant conclusions about the sensor\u27s immunity to environmental noise are difficult to draw, the results suggest that the TERC sensor is considerably more resistant to the effects of noise than typical acoustic sensors, making it a valuable addition to the multi-sensor speech processing approach

Analysis and correction of the helium speech effect by autoregressive signal processing

SIGLELD:D48902/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Speech produced in noise: Relationship between listening difficulty and acoustic and durational parameters.

Conversational speech produced in noise can be characterised by increases in intelligibility relative to such speech produced in quiet. Listening difficulty (LD) is a metric that can be used to evaluate speech transmission performance more sensitively than intelligibility scores in situations in which performance is likely to be high. The objectives of the present study were to evaluate the LD of speech produced in different noise and style conditions, to evaluate the spectral and durational speech modifications associated with these conditions, and to determine whether any of the spectral and durational parameters predicted LD. Nineteen subjects were instructed to speak at normal and loud volumes in the presence of background noise at 40.5 dB(A) and babble noise at 61 dB(A). The speech signals were amplitude-normalised, combined with pink noise to obtain a signal-to-noise ratio of -6 dB, and presented to twenty raters who judged their LD. Vowel duration, fundamental frequency and the proportion of the spectral energy in high vs low frequencies increased with the noise level within both styles. LD was lowest when the speech was produced in the presence of high level noise and at a loud volume, indicating improved intelligibility. Spectrum balance was observed to predict LD