Acoustic Speech Signal Research Articles

Magnetomotive Displacement of the Tympanic Membrane Using Magnetic Nanoparticles: Toward Enhancement of Sound Perception.

A novel hearing-aid scheme using magnetomotive nanoparticles (MNPs) as transducers in the tympanic membrane (TM) is proposed, aiming to noninvasively and directly induce a modulated vibration on the TM. In this feasibility study, iron oxide (Fe3O4) nanoparticles were applied on ex vivo rat TM tissues and allowed to diffuse over ∼2 h. Subsequently, magnetic force was exerted on the MNP-laden TM via a programmable electromagnetic solenoid to induce the magnetomotion. Optical coherence tomography (OCT), along with its phase-sensitive measurement capabilities, was utilized to visualize and quantify the nanometer-scale vibrations generated on the TM tissues. The magnetomotive displacements induced on the TM were significantly greater than the baseline vibration of the TM without MNPs. In addition to a pure frequency tone, a chirped excitation and the corresponding spectroscopic response were also successfully generated and obtained. Finally, visualization of volumetric TM dynamics was achieved. This study demonstrates the effectiveness of magnetically inducing vibrations on TMs containing iron oxide nanoparticles, manipulating the amplitude and the frequency of the induced TM motions, and the capability of assessing the magnetomotive dynamics via OCT. The results demonstrated here suggest the potential use of this noninvasive magnetomotive approach in future hearing aid applications. OCT can be utilized to investigate the magnetomotive dynamics of the TM, which may either enhance sound perception or magnetically induce the perception of sound without the need for acoustic speech signals.

Listening Effort: How the Cognitive Consequences of Acoustic Challenge Are Reflected in Brain and Behavior.

Everyday conversation frequently includes challenges to the clarity of the acoustic speech signal, including hearing impairment, background noise, and foreign accents. Although an obvious problem is the increased risk of making word identification errors, extracting meaning from a degraded acoustic signal is also cognitively demanding, which contributes to increased listening effort. The concepts of cognitive demand and listening effort are critical in understanding the challenges listeners face in comprehension, which are not fully predicted by audiometric measures. In this article, the authors review converging behavioral, pupillometric, and neuroimaging evidence that understanding acoustically degraded speech requires additional cognitive support and that this cognitive load can interfere with other operations such as language processing and memory for what has been heard. Behaviorally, acoustic challenge is associated with increased errors in speech understanding, poorer performance on concurrent secondary tasks, more difficulty processing linguistically complex sentences, and reduced memory for verbal material. Measures of pupil dilation support the challenge associated with processing a degraded acoustic signal, indirectly reflecting an increase in neural activity. Finally, functional brain imaging reveals that the neural resources required to understand degraded speech extend beyond traditional perisylvian language networks, most commonly including regions of prefrontal cortex, premotor cortex, and the cingulo-opercular network. Far from being exclusively an auditory problem, acoustic degradation presents listeners with a systems-level challenge that requires the allocation of executive cognitive resources. An important point is that a number of dissociable processes can be engaged to understand degraded speech, including verbal working memory and attention-based performance monitoring. The specific resources required likely differ as a function of the acoustic, linguistic, and cognitive demands of the task, as well as individual differences in listeners’ abilities. A greater appreciation of cognitive contributions to processing degraded speech is critical in understanding individual differences in comprehension ability, variability in the efficacy of assistive devices, and guiding rehabilitation approaches to reducing listening effort and facilitating communication.

Neural Entrainment to Speech Modulates Speech Intelligibility

Speech is crucial for communication in everyday life.Speech-brain entrainment, the alignment of neural activity to the slow temporal fluctuations (envelope) of acoustic speech input, is a ubiquitous element of current theories of speech processing. Associations between speech-brain entrainment and acoustic speech signal, listening task, and speech intelligibility have been observed repeatedly.However, a methodological bottleneck has prevented so far clarifying whether speech-brain entrainment contributes functionally to (i.e., causes) speech intelligibility or is merely an epiphenomenon of it. To address this long-standing issue, we experimentally manipulated speech-brain entrainment without concomitant acoustic and task-related variations, using a brain stimulation approach that enables modulating listeners' neural activity with transcranial currents carrying speech-envelope information. Results from two experiments involving a cocktail-party-like scenario and a listening situation devoid of aural speech-amplitude envelope input reveal consistent effects on listeners' speech-recognition performance, demonstrating a causal role of speech-brain entrainment in speech intelligibility. Our findings imply that speech-brain entrainment is critical for auditory speech comprehensionand suggest that transcranial stimulation with speech-envelope-shaped currents can be utilized to modulate speech comprehension in impaired listening conditions.

Acoustic simulation of cochlear implant hearing: Effect of manipulating various acoustic parameters on intelligibility of speech

Objective: Cochlear implants process the acoustic speech signal and convert it into electrical impulses. During this processing, many parameters contribute to speech perception. The available literature reviewed the effect of manipulating one or two such parameters on speech intelligibility, but multiple parameters are seldom manipulated.Method: Acoustic parameters, including pulse rate, number of channels, ‘n of m’, number of electrodes, and channel spacing, were manipulated in acoustic simulations of cochlear implant hearing and 90 different combinations were created. Speech intelligibility at sentence level was measured using subjective and objective tests.Results: Principal component analysis was employed to select only those components with maximum factor loading, thus reducing the number of components to a reasonable limit. Perceptual speech intelligibility was maximum for signal processing manipulation with respect to ‘n of m’ and rate of pulses/sec. Regression analysis revealed that lower rate (=500 pps/ch) and lesser stimulating electrodes per cycle (2–4) contributed maximally for speech intelligibility. Perceptual estimate of speech quality (PESQ) and composite measures of spectral weights and likelihood ratio correlated with subjective speech intelligibility scores.Discussion: The findings are consistent with the literature review, indicating that lesser stimulated channel per cycle reduces electrode interaction and hence improve spectral resolution of speech. Reduced rate of pulses/second enhances temporal resolution of speech. Thus, these two components contribute significantly to speech intelligibility.Conclusion: Pulse rate/channel and ‘n of m’ contribute maximally to speech intelligibility, at least in simulations of electric hearing.

Predicting couple therapy outcomes based on speech acoustic features.

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral outcome, like many other behavioral aspects, is closely related to the dynamics and mutual influence of the interlocutors during their interaction and their resulting behavioral patterns.

P 102 Domain-specific effects of transcranial random noise stimulation (tRNS) on auditory feature processing

Theoretical background Decoding acoustic speech signals requires to parse the acoustic stream into linguistically meaningful units, and to extract stress and prosody. Accordingly, the temporal and spectral resolution of the auditory cortex is essential in successful speech processing ( Rosen, 1992 ). The reduced acuity in discriminating these features is considered as key factor in dyslexia ( Goswami, 2011 ) but is also a consequence of normal aging ( Walton, 2010 ). Transcranial random noise stimulation (tRNS), a non-invasive brain stimulation technique that applies weak currents at multiple frequencies, is assumed to modulate the excitability of neural assemblies and, in consequence, the reactivity of cortical structures. However, the effect of tRNS on the auditory system is yet unclear. The present study aimed to fill this gap by investigating the impact of tRNS on basic auditory feature processing. Considering the hemispheric specialization of the auditory system in analyzing theses features we hypothesized that a site-specific tRNS application leads to domain-specific changes in auditory features processing. In concrete, tRNS over the left auditory cortex (lAC) was hypothesized to affect the temporal domain while tRNS over the right auditory cortex (rAC) was assumed to modulate the spectral domain. Finally, we investigated whether tRNS over bilateral auditory cortex (bAC) leads to a modulation of both dimensions. Methods On four days 18 healthy, right-handed adults received 30 min. tRNS at an intensity of 1.5 mA either over the lAC, over the rAC, over bAC or sham stimulation while continuous EEG was recorded. The task consisted of the detection of a deviant tone in a sequence of three sine wave tones. While the duration of all tones and the pitch of the standard tones was kept stable, the inter-tone interval (temporal information) and the frequency in that the deviant tone differed from the standard tones (spectral information) was modulated. Results In result, we found that tRNS applied over the lAC and over bAC inferred with task performance. This effect was specifically modulated by the inter-tone interval between the tone triplets but not by the frequency of the deviant tone. On the electrophysiological level, this finding was paralleled by feature-specific brain response patterns in the alpha and the lower gamma band. Discussion Our findings demonstrate the functional relevance of the lAC in processing temporal features. Most importantly, we proved tRNS as a valid technique to modulate basic auditory perception. The current results stress the need for further investigations on the effect of tRNS in the auditory domain. Systematically assessing the most effective stimulation parameters provides the basis for the clinical application of tRNS in the context of speech and language disorders.

Web Recognition of Spoken Hindi

Technology has evolved and computers have but still Indian communities are far from the use of computers, only 37% [13] user of Indian society like persons from academics, health, engineering and research make use of the benefits of computer. Although there is a revolution in development of operating systems in the past two years, there are no operating systems that support Indian languages like Hindi, they only support English language and people who know English can work on these. This work shows the development of speaker independent ASR system for Indian speaker of a particular type i.e. spoken Hindi. Objective: An automated system is developed which accept Hindi query from the user and search the keywords from the query on web and shows the result accordingly. A bilingual lexicon of 3145 words was built. The techniques to convert acoustic speech signals into strings of words were also designed. The dictionary maintains the POS of source to target language and CFG grammar based model for Hindi language was developed. Methods: A speech database consisting of speech files is created at the rate of 16 KHz. Interviews were conducted uto record speech files from users for developing the speech database for training the system. The system takes spoken Hindi as input and generates Hindi sentences. Hindi sentences are parsed with the help of Top-Down Paser and identify the keywords and form a exact query with the help of grammar rules which are given as input to Google search engine to search the result as per query and display on screen. The lexical categories of Hindi language in grammar are done on the bases of verb, noun, preposition, adjective, adverb phrases. Result: A total of 101 speakers from Faridabad district, comprising of both males and females took part in data collection process. The system was tested in a sound proof room and achieved 88.05% accuracy. It is observed that utilization of more training data together with detailed modeling of speech signals can improve the system performance to a level adequate for actual deployment. Application/Improvements: This system provides access to a computer system or to digital content over the internet to illiterate, vision-impaired, urban and semi-urban Indian people who are not able to read/write English language. The same speech interface can be enhanced to work in other regional languages of India like Punjabi, Marathi and Telugu, etc. To enhance or to extend it for other languages, the transfer or translation rules of grammar is needed, which can be generated with great ease by using the same dataset with different target languages. Keywords: Corpus, Hindi, Recognition, Speech, Web

Alternating minimisation for glottal inverse filtering

A new method is proposed for solving the glottal inverse filtering (GIF) problem. The goal of GIF is to separate an acoustical speech signal into two parts: the glottal airflow excitation and the vocal tract filter. To recover such information one has to deal with a blind deconvolution problem. This ill-posed inverse problem is solved under a deterministic setting, considering unknowns on both sides of the underlying operator equation. A stable reconstruction is obtained using a double regularization strategy, alternating between fixing either the glottal source signal or the vocal tract filter. This enables not only splitting the nonlinear and nonconvex problem into two linear and convex problems, but also allows the use of the best parameters and constraints to recover each variable at a time. This new technique, called alternating minimization glottal inverse filtering (AM-GIF), is compared with two other approaches: Markov chain Monte Carlo glottal inverse filtering (MCMC-GIF), and iterative adaptive inverse filtering (IAIF), using synthetic speech signals. The recent MCMC-GIF has good reconstruction quality but high computational cost. The state-of-the-art IAIF method is computationally fast but its accuracy deteriorates, particularly for speech signals of high fundamental frequency (F0). The results show the competitive performance of the new method: With high F0, the reconstruction quality is better than that of IAIF and close to MCMC-GIF while reducing the computational complexity by two orders of magnitude.

Quadratic Programming Approach to Glottal Inverse Filtering by Joint Norm-1 and Norm-2 Optimization

This study proposes an approach for glottal inverse filtering of acoustic speech signals using quadratic programming (QPR). The method aims to jointly model the effect of vocal tract and lip radiation with a single filter whose coefficients are optimized using QPR. This optimization is based on the principles of closed phase analysis, where the contribution of the glottal source is attenuated in optimizing the inverse model of the vocal tract. By expressing the optimization problem in terms of the output of a filter, we can apply physically motivated optimization such as flatness of the closed phase. The proposed method was objectively evaluated using a synthetic Liljencrants–Fant model based test set of sustained vowels, as well as a real speech test set where the glottal flow estimates’ closed phases were compared in terms of their flatness. The results based on synthetic speech indicate that the proposed method is robust to changes in $f_0$ , and state-of-the-art quality results were obtained for high-pitched voices, when $f_0$ is in the range 330–450 Hz. The results based on real speech indicate that the proposed method produces glottal flow estimates that have flatter closed phases with less formant ripple in comparison to estimates computed with known reference methods.

Inversion of speech by non-linear transformation of temporary

Electromagnetic Articulography (EMA) is a precise method for speech articulators assessment which is carried out by sensors placed mainly on the tongue. Various methods are being developed in order to avoid the assessment by EMA sensors. One of them is speech inversion. Here preliminary research on speech inversion based on dynamic time warping (DTW) method has been described. Mel-frequency cepstral coefficients (MFCC) method has been chosen as the acoustic speech signal parametrization method. Root mean square errors (RMSE) of the evaluation have been presented and discussed.

Associations between tongue movement pattern consistency and formant movement pattern consistency in response to speech behavioral modifications.

The degree of speech movement pattern consistency can provide information about speech motor control. Although tongue motor control is particularly important because of the tongue's primary contribution to the speech acoustic signal, capturing tongue movements during speech remains difficult and costly. This study sought to determine if formant movements could be used to estimate tongue movement pattern consistency indirectly. Two age groups (seven young adults and seven older adults) and six speech conditions (typical, slow, loud, clear, fast, bite block speech) were selected to elicit an age- and task-dependent performance range in tongue movement pattern consistency. Kinematic and acoustic spatiotemporal indexes (STI) were calculated based on sentence-length tongue movement and formant movement signals, respectively. Kinematic and acoustic STI values showed strong associations across talkers and moderate to strong associations for each talker across speech tasks; although, in cases where task-related tongue motor performance changes were relatively small, the acoustic STI values were poorly associated with kinematic STI values. These findings suggest that, depending on the sensitivity needs, formant movement pattern consistency could be used in lieu of direct kinematic analysis to indirectly examine speech motor control.

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There have being great efforts made in the development of automated Instrumentation system for speech recognition (AISR) to provide a two-way communication between deaf and vocal people. This system performance achievable with the output of current real-time speech recognition systems would be extremely poor relative to normal speech reception. An alternate application of AISR technology to aid the hearing impaired would derive cues from the acoustical speech signal that could be used to supplement speechreading. We propose a study of highly trained receivers of speech signal that indicates that nearly perfect reception of everyday connected speech materials can be achieved at near normal speaking rates. To understand the accuracy that might be achieved with automatically generated cue symbols for visual representation. The system uses (HMM) for recognition of voiced data & Euclidian distance approach for sign language. The proposed task is a complementary work to the ongoing research work for recognizing the finger movement of a vocally disabled person to speech signal called. A New communication Paradigm: “Action-To-Speech”

Eye Can Hear Clearly Now: Inverse Effectiveness in Natural Audiovisual Speech Processing Relies on Long-Term Crossmodal Temporal Integration.

Speech comprehension is improved by viewing a speaker's face, especially in adverse hearing conditions, a principle known as inverse effectiveness. However, the neural mechanisms that help to optimize how we integrate auditory and visual speech in such suboptimal conversational environments are not yet fully understood. Using human EEG recordings, we examined how visual speech enhances the cortical representation of auditory speech at a signal-to-noise ratio that maximized the perceptual benefit conferred by multisensory processing relative to unisensory processing. We found that the influence of visual input on the neural tracking of the audio speech signal was significantly greater in noisy than in quiet listening conditions, consistent with the principle of inverse effectiveness. Although envelope tracking during audio-only speech was greatly reduced by background noise at an early processing stage, it was markedly restored by the addition of visual speech input. In background noise, multisensory integration occurred at much lower frequencies and was shown to predict the multisensory gain in behavioral performance at a time lag of ∼250 ms. Critically, we demonstrated that inverse effectiveness, in the context of natural audiovisual (AV) speech processing, relies on crossmodal integration over long temporal windows. Our findings suggest that disparate integration mechanisms contribute to the efficient processing of AV speech in background noise. The behavioral benefit of seeing a speaker's face during conversation is especially pronounced in challenging listening environments. However, the neural mechanisms underlying this phenomenon, known as inverse effectiveness, have not yet been established. Here, we examine this in the human brain using natural speech-in-noise stimuli that were designed specifically to maximize the behavioral benefit of audiovisual (AV) speech. We find that this benefit arises from our ability to integrate multimodal information over longer periods of time. Our data also suggest that the addition of visual speech restores early tracking of the acoustic speech signal during excessive background noise. These findings support and extend current mechanistic perspectives on AV speech perception.

The influence of head and body postures on the acoustic speech signal

The influence of head and body postures on the acoustic speech signal

Symbol emergence in robotics: a survey

Humans can learn a language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form symbol systems and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted regarding the construction of robotic systems and machine learning methods that can learn a language through embodied multimodal interaction with their environment and other systems. Understanding human?-social interactions and developing a robot that can smoothly communicate with human users in the long term require an understanding of the dynamics of symbol systems. The embodied cognition and social interaction of participants gradually alter a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER represents a constructive approach towards a symbol emergence system. The symbol emergence system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e. humans and developmental robots. In this paper, specifically, we describe some state-of-art research topics concerning SER, such as multimodal categorization, word discovery, and double articulation analysis. They enable robots to discover words and their embodied meanings from raw sensory-motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions for research in SER.

A silent speech system based on permanent magnet articulography and direct synthesis

In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies.

"Perception of the speech code" revisited: Speech is alphabetic after all.

We revisit an article, "Perception of the Speech Code" (PSC), published in this journal 50 years ago (Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967) and address one of its legacies concerning the status of phonetic segments, which persists in theories of speech today. In the perspective of PSC, segments both exist (in language as known) and do not exist (in articulation or the acoustic speech signal). Findings interpreted as showing that speech is not a sound alphabet, but, rather, phonemes are encoded in the signal, coupled with findings that listeners perceive articulation, led to the motor theory of speech perception, a highly controversial legacy of PSC. However, a second legacy, the paradoxical perspective on segments has been mostly unquestioned. We remove the paradox by offering an alternative supported by converging evidence that segments exist in language both as known and as used. We support the existence of segments in both language knowledge and in production by showing that phonetic segments are articulatory and dynamic and that coarticulation does not eliminate them. We show that segments leave an acoustic signature that listeners can track. This suggests that speech is well-adapted to public communication in facilitating, not creating a barrier to, exchange of language forms.

Automatic speech segmentation in syllable centric speech recognition system

Speech recognition is the process of understanding the human or natural language speech by a computer. A syllable centric speech recognition system in this aspect identifies the syllable boundaries in the input speech and converts it into the respective written scripts or text units. Appropriate segmentation of the acoustic speech signal into syllabic units is an important task for development of highly accurate speech recognition system. This paper presents an automatic syllable based segmentation technique for segmenting continuous speech signals in Indian languages at syllable boundaries. To analyze the performance of the proposed technique, a set of experiments are carried out on different speech samples in three Indian languages Hindi, Bengali and Odia and are compared with the existing group delay based segmentation technique along with the manual segmentation technique. The results of all our experiments show the effectiveness of the proposed technique in segmenting the syllable units from the original speech samples compared to the existing techniques.

Performance Analysis of Windowing Techniques in Automatic Speech Signal Segmentation

Background/Objectives: Automatic Speech Recognition (ASR) and Language Identification (LID) are the key areas of acoustic speech signal processing. Speech signals watermarking, steganography and cryptography are considered to be the emerging techniques to ensure information security in speech signal transmission. Efforts should be taken to retain every minute detail of the signal. In all above mentioned methods, it is necessary to preprocess the speech signal so as to get best results. To take into account, this paper presents the performance analysis of windowing techniques in automatic speech signal segmentation. Methods/Statistical Analysis: Speech signal is segmented into syllables as a first step. In the process of segmentation, a windowing technique is applied to enhance the syllable boundaries. Then window function is applied to the input signal before Discrete Fourier Transform has applied. There are many windowing techniques available. Proposed work is carried to analyze the performance of few windowing functions in order to retain every minute detail of the signal and to preprocess the speech signal effectively in the Automatic Speech Recognition (ASR) and Language Identification (LID). Findings: The results produced by the windowing and filtering techniques in segmentation process are plotted. The proposed method out performs well and the performance of different windowing and filtering techniques is analyzed. The number of peaks found is tabulated. Application/Improvements: Thus our experimental results shows the significance of segmenting speech signals effectively using windowing function with discrete filters than the adaptive filters. Further those segments can be used in the field of Automatic Speech Recognition (ASR), Language Identification (LID), Speech signals watermarking, steganography. The observed segments with windowing using discrete filters are highly useful for clustering and pattern matching techniques

Temporal effects in priming of masked and degraded speech.

When listeners know the content of the message they are about to hear, the clarity of distorted or partially masked speech increases dramatically. The current experiments investigated this priming phenomenon quantitatively using a same-different task where a typed caption and auditory message either matched exactly or differed by one key word. Four conditions were tested with groups of normal-hearing listeners: (a) natural speech presented in two-talker babble in a non-spatial configuration, (b) same as (a) but with the masker time reversed, (c) same as (a) but with target-masker spatial separation, and (d) vocoded sentences presented in speech-spectrum noise. The primary manipulation was the timing of the caption relative to the auditory message, which varied in 20 steps with a resolution of 200 ms. Across all four conditions, optimal performance was achieved when the initiation of the text preceded the acoustic speech signal by at least 400 ms, driven mostly by a low number of "different" responses to Same stimuli. Performance was slightly poorer with simultaneous delivery and much poorer when the auditory signal preceded the caption. Because priming may be used to facilitate perceptual learning, identifying optimal temporal conditions for priming could help determine the best conditions for auditory training.