90 research outputs found

    Synergy of Acoustic-Phonetics and Auditory Modeling Towards Robust Speech Recognition

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    The problem addressed in this work is that of enhancing speech signals corrupted by additive noise and improving the performance of automatic speech recognizers in noisy conditions. The enhanced speech signals can also improve the intelligibility of speech in noisy conditions for human listeners with hearing impairment as well as for normal listeners. The original Phase Opponency (PO) model, proposed to detect tones in noise, simulates the processing of the information in neural discharge times and exploits the frequency-dependent phase properties of the tuned filters in the auditory periphery along with the cross-auditory-nerve-fiber coincidence detection to extract temporal cues. The Modified Phase Opponency (MPO) proposed here alters the components of the PO model in such a way that the basic functionality of the PO model is maintained but the various properties of the model can be analyzed and modified independently of each other. This work presents a detailed mathematical formulation of the MPO model and the relation between the properties of the narrowband signal that needs to be detected and the properties of the MPO model. The MPO speech enhancement scheme is based on the premise that speech signals are composed of a combination of narrow band signals (i.e. harmonics) with varying amplitudes. The MPO enhancement scheme outperforms many of the other speech enhancement techniques when evaluated using different objective quality measures. Automatic speech recognition experiments show that replacing noisy speech signals by the corresponding MPO-enhanced speech signals leads to an improvement in the recognition accuracies at low SNRs. The amount of improvement varies with the type of the corrupting noise. Perceptual experiments indicate that: (a) there is little perceptual difference in the MPO-processed clean speech signals and the corresponding original clean signals and (b) the MPO-enhanced speech signals are preferred over the output of the other enhancement methods when the speech signals are corrupted by subway noise but the outputs of the other enhancement schemes are preferred when the speech signals are corrupted by car noise

    Effects of Hearing Aid Amplification on Robust Neural Coding of Speech

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    Hearing aids are able to restore some hearing abilities for people with auditory impairments, but background noise remains a significant problem. Unfortunately, we know very little about how speech is encoded in the auditory system, particularly in impaired systems with prosthetic amplifiers. There is growing evidence that relative timing in the neural signals (known as spatiotemporal coding) is important for speech perception, but there is little research that relates spatiotemporal coding and hearing aid amplification. This research uses a combination of computational modeling and physiological experiments to characterize how hearing aids affect vowel coding in noise at the level of the auditory nerve. The results indicate that sensorineural hearing impairment degrades the temporal cues transmitted from the ear to the brain. Two hearing aid strategies (linear gain and wide dynamic-range compression) were used to amplify the acoustic signal. Although appropriate gain was shown to improve temporal coding for individual auditory nerve fibers, neither strategy improved spatiotemporal cues. Previous work has attempted to correct the relative timing by adding frequency-dependent delays to the acoustic signal (e.g., within a hearing aid). We show that, although this strategy can affect the timing of auditory nerve responses, it is unlikely to improve the relative timing as intended. We have shown that existing hearing aid technologies do not improve some of the neural cues that we think are important for perception, but it is important to understand these limitations. Our hope is that this knowledge can be used to develop new technologies to improve auditory perception in difficult acoustic environments

    SEGREGATION OF SPEECH SIGNALS IN NOISY ENVIRONMENTS

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    Automatic segregation of overlapping speech signals from single-channel recordings is a challenging problem in speech processing. Similarly, the problem of extracting speech signals from noisy speech is a problem that has attracted a variety of research for several years but is still unsolved. Speech extraction from noisy speech mixtures where the background interference could be either speech or noise is especially difficult when the task is to preserve perceptually salient properties of the recovered acoustic signals for use in human communication. In this work, we propose a speech segregation algorithm that can simultaneously deal with both background noise as well as interfering speech. We propose a feature-based, bottom-up algorithm which makes no assumptions about the nature of the interference or does not rely on any prior trained source models for speech extraction. As such, the algorithm should be applicable for a wide variety of problems, and also be useful for human communication since an aim of the system is to recover the target speech signals in the acoustic domain. The proposed algorithm can be compartmentalized into (1) a multi-pitch detection stage which extracts the pitch of the participating speakers, (2) a segregation stage which teases apart the harmonics of the participating sources, (3) a reliability and add-back stage which scales the estimates based on their reliability and adds back appropriate amounts of aperiodic energy for the unvoiced regions of speech and (4) a speaker assignment stage which assigns the extracted speech signals to their appropriate respective sources. The pitch of two overlapping speakers is extracted using a novel feature, the 2-D Average Magnitude Difference Function, which is also capable of giving a single pitch estimate when the input contains only one speaker. The segregation algorithm is based on a least squares framework relying on the estimated pitch values to give estimates of each speaker's contributions to the mixture. The reliability block is based on a non-linear function of the energy of the estimates, this non-linear function having been learnt from a variety of speech and noise data but being very generic in nature and applicability to different databases. With both single- and multiple- pitch extraction and segregation capabilities, the proposed algorithm is amenable to both speech-in-speech and speech-in-noise conditions. The algorithm is evaluated on several objective and subjective tests using both speech and noise interference from different databases. The proposed speech segregation system demonstrates performance comparable to or better than the state-of-the-art on most of the objective tasks. Subjective tests on the speech signals reconstructed by the algorithm, on normal hearing as well as users of hearing aids, indicate a significant improvement in the perceptual quality of the speech signal after being processed by our proposed algorithm, and suggest that the proposed segregation algorithm can be used as a pre-processing block within the signal processing of communication devices. The utility of the algorithm for both perceptual and automatic tasks, based on a single-channel solution, makes it a unique speech extraction tool and a first of its kind in contemporary technology

    Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex

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    How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852

    New Stategies for Single-channel Speech Separation

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    ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION

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    Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system

    Peripheral auditory processing and speech reception in impaired hearing

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    Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

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    This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.National Science Foundation (IRI-87-16960, IRI-87-6960); Air Force Office of Scientific Research (90-0175); Defense Advanced Research Projects Agency (90-0083
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