Effects of noise suppression and envelope dynamic range compression on the intelligibility of vocoded sentences for a tonal language

Vocoder simulation studies have suggested that the carrier signal type employed affects the intelligibility of vocoded speech. The present work further assessed how carrier signal type interacts with additional signal processing, namely, single-channel noise suppression and envelope dynamic range compression, in determining the intelligibility of vocoder simulations. In Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) or two-talker babble (2TB) were processed by one of four single-channel noise-suppression algorithms before undergoing tone-vocoded (TV) or noise-vocoded (NV) processing. In Experiment 2, dynamic ranges of multiband envelope waveforms were compressed by scaling of the mean-removed envelope waveforms with a compression factor before undergoing TV or NV processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed vocoded speech depended on the masker type (SSN vs 2TB). NV speech was more negatively influenced by envelope dynamic range compression than was TV speech. These findings suggest that an interactional effect exists between the carrier signal type employed in the vocoding process and envelope distortion caused by signal processing

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

Improving the Speech Intelligibility By Cochlear Implant Users

In this thesis, we focus on improving the intelligibility of speech for cochlear implants (CI) users. As an auditory prosthetic device, CI can restore hearing sensations for most patients with profound hearing loss in both ears in a quiet background. However, CI users still have serious problems in understanding speech in noisy and reverberant environments. Also, bandwidth limitation, missing temporal fine structures, and reduced spectral resolution due to a limited number of electrodes are other factors that raise the difficulty of hearing in noisy conditions for CI users, regardless of the type of noise. To mitigate these difficulties for CI listener, we investigate several contributing factors such as the effects of low harmonics on tone identification in natural and vocoded speech, the contribution of matched envelope dynamic range to the binaural benefits and contribution of low-frequency harmonics to tone identification in quiet and six-talker babble background. These results revealed several promising methods for improving speech intelligibility for CI patients. In addition, we investigate the benefits of voice conversion in improving speech intelligibility for CI users, which was motivated by an earlier study showing that familiarity with a talker’s voice can improve understanding of the conversation. Research has shown that when adults are familiar with someone’s voice, they can more accurately – and even more quickly – process and understand what the person is saying. This theory identified as the “familiar talker advantage” was our motivation to examine its effect on CI patients using voice conversion technique. In the present research, we propose a new method based on multi-channel voice conversion to improve the intelligibility of transformed speeches for CI patients

A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise

The potential contribution of the peripheral auditory efferent system to our understanding of speech in a background of competing noise was studied using a computer model of the auditory periphery and assessed using an automatic speech recognition system. A previous study had shown that a fixed efferent attenuation applied to all channels of a multi-channel model could improve the recognition of connected digit triplets in noise [G. J. Brown, R. T. Ferry, and R. Meddis, J. Acoust. Soc. Am. 127, 943?954 (2010)]. In the current study an anatomically justified feedback loop was used to automatically regulate separate attenuation values for each auditory channel. This arrangement resulted in a further enhancement of speech recognition over fixed-attenuation conditions. Comparisons between multi-talker babble and pink noise interference conditions suggest that the benefit originates from the model?s ability to modify the amount of suppression in each channel separately according to the spectral shape of the interfering sounds

Speech enhancement using auditory filterbank.

This thesis presents a novel subband noise reduction technique for speech enhancement, termed as Adaptive Subband Wiener Filtering (ASWF), based on a critical-band gammatone filterbank. The ASWF is derived from a generalized Subband Wiener Filtering (SWF) equation and reduces noises according to the estimated signal-to-noise ratio (SNR) in each auditory channel and in each time frame. The design of a subband noise estimator, suitable for some real-life noise environments, is also presented. This denoising technique would be beneficial for some auditory-based speech and audio applications, e.g. to enhance the robustness of sound processing in cochlear implants. Comprehensive objective and subjective tests demonstrated the proposed technique is effective to improve the perceptual quality of enhanced speeches. This technique offers a time-domain noise reduction scheme using a linear filterbank structure and can be combined with other filterbank algorithms (such as for speech recognition and coding) as a front-end processing step immediately after the analysis filterbank, to increase the robustness of the respective application.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .G85. Source: Masters Abstracts International, Volume: 44-03, page: 1452. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

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

Noise processing in the auditory system with applications in speech enhancement

Abstract: The auditory system is extremely efficient in extracting auditory information in the presence of background noise. However, speech enhancement algorithms, aimed at removing the background noise from a degraded speech signal, are not achieving results that are near the efficacy of the auditory system. The purpose of this study is thus to first investigate how noise affects the spiking activity of neurons in the auditory system and then use the brain activity in the presence of noise to design better speech enhancement algorithms. In order to investigate how noise affects the spiking activity of neurons, we first design a generalized linear model that relates the spiking activity of neurons to intrinsic and extrinsic covariates that can affect their activity, such as noise. From this model, we extract two metrics, one that shows the effects of noise on the spiking activity and another the relative effects of vocalization compared to noise. We use these metrics to analyze neural data, recorded from a structure of the auditory system named the inferior colliculus (IC), while presenting noisy vocalizations. We studied the effect of different kinds of noises (non-stationary, white and natural stationary), different vocalizations, different input sound levels and signal-to-noise ratios (SNR). We found that the presence of non-stationary noise increases the spiking activity of neurons, regardless of the SNR, input level or vocalization type. The presence of white or natural stationary noises however causes a great diversity of responses where the activity of sites could increase, decrease or remain unchanged. This shows that the noise invariance previously reported in the IC depends on the noisy conditions, which had not been observed before. We then address the problem of speech enhancement using information from the brain's processing in the presence of noise. It has been shown before that the brain waves of a listener strongly correlates with the speaker to which the listener attends. Given this, we design two speech enhancement algorithms with a denoising autoencoder structure, namely the Brain Enhanced Speech Denoiser (BESD) and U-shaped Brain Enhanced Speech Denoiser (U-BESD). These algorithms take advantage of the attended auditory information present in the brain activity of the listener to denoise a multi-talker speech. The U-BESD is built upon the BESD with the addition of skip connections and dilated convolutions. Compared to previously proposed approaches, BESD and U-BESD are trained in a single neural architecture, lowering the complexity of the algorithm. We investigate two experimental settings. In the first one, the attended speaker is known, referred to as the speaker-specific setting, and in the second one no prior information is available about the attended speaker, referred to as the speaker-independent setting. In the speaker-specific setting, we show that both the BESD and U-BESD algorithms surpass a similar denoising autoencoder. Moreover, we also show that in the speaker-independent setting, U-BESD surpasses the performance of the only known approach that also uses the brain's activity.Le système auditif est extrêmement efficace pour extraire de l’information pertinente en présence d’un bruit de fond. Par contre, les algorithmes de rehaussement de la parole, visant à supprimer le bruit d’un signal de parole bruité, n’atteignent pas des résultats proches de l’efficacité du système auditif. Le but de cette étude est donc d’abord d’étudier comment le bruit affecte l’activité neuronale dans le système auditif, puis d’utiliser l’activité cérébrale en présence de bruit pour concevoir de meilleurs algorithmes de rehaussement. Afin d’étudier comment le bruit peut affecter l’activité des neurones, nous concevons d’abord un modèle linéaire généralisé qui relie l’activité des neurones aux covariables intrinsèques et extrinsèques qui peuvent affecter leur activité, comme le bruit. De ce modèle, nous extrayons deux métriques, l’une qui permet d’étudier les effets du bruit sur l’activité neuronale et l’autre les effets relatifs sur cette activité de la vocalisation par rapport au bruit. Nous utilisons ces métriques pour analyser l’activité neuronale d’une structure du système auditif, nomée le colliculus inférieur (IC), enregistrée lors de la présentation de vocalisations bruitées. Nous avons étudié l’effet de différents types de bruits, différentes vocalisations, différents niveaux sonores d’entrée et différents rapports signal sur bruit (SNR). Nous avons constaté que la présence de bruit non stationnaire augmente l’activité des neurones, quel que soit le SNR, le niveau d’entrée ou le type de vocalisation. La présence de bruits stationnaires blancs ou naturels provoque cependant une grande diversité de réponses où l’activité des sites d’enregistrement pouvait augmenter, diminuer ou rester inchangée. Cela montre que l’invariance du bruit précédemment signalée dans l’IC dépend des conditions de bruit, ce qui n’avait pas été observé auparavant. Nous abordons ensuite le problème du rehaussement de la parole en utilisant de l’information provenant du cerveau. Il a été démontré auparavant que les ondes cérébrales d’un auditeur sont fortement corrélées avec le locuteur auquel l’auditeur porte attention. Compte tenu de cette corrélation, nous concevons deux algorithmes de rehaussement de la parole, le Brain Enhanced Speech Denoiser (BESD) et le U-shaped Brain Enhanced Speech Denoiser (U-BESD), qui tirent parti de l’information présente dans l’activité cérébrale de l’auditeur pour débruiter un signal de parole multi-locuteurs. L’U-BESD est construit à partir du BESD avec l’ajout de sauts de connexions (skip connections) et de convolutions dilatées. De plus, BESD et U-BESD sont constitués respectivement d’un seul réseau qui nécessite un seul entraînement, ce qui réduit la complexité de l’algorithme en comparaison avec les approches existantes. Nous étudions deux conditions expérimentales. Dans la première, le locuteur auquel l’auditeur porte attention est connu, et dans la seconde, ce locuteur n’est pas connu. Dans le cadre du locuteur connu, nous montrons que les algorithmes BESD et U-BESD surpassent un autoencodeur similaire. De plus, nous montrons également que dans le cadre du locuteur inconnu, le U-BESD surpasse les performances de la seule approche existante connue qui utilise également l’activité cérébrale

Coping with phonological assimilation in speech perception : evidence for early compensation

The pronunciation of the same word may vary considerably as a consequence of its context. The Dutch word tuin (English, garden) may be pronounced tuim if followed by bank (English, bench), but not if followed by stoel (English, chair). In a series of four experiments, we examined how Dutch listeners cope with this context sensitivity in their native language. A first word identification experiment showed that the perception of a word-final nasal depends on the subsequent context. Viable assimilations, but not unviable assimilations, were often confused perceptually with canonical word forms in a word identification task. Two control experiments ruled out the possibility that this effect was caused by perceptual masking or was influenced by lexical top-down effects. A passive-listening study in which electrophysiological measurements were used showed that only unviable, but not viable, phonological changes elicited a significant mismatch negativity. The results indicate that phonological assimilations are dealt with by an early prelexical mechanism.peer-reviewe