Silent-speech enhancement using body-conducted vocal-tract resonance signals

The physical characteristics of weak body-conducted vocal-tract resonance signals called non-audible murmur (NAM) and the acoustic characteristics of three sensors developed for detecting these signals have been investigated. NAM signals attenuate 50 dB at 1 kHz; this attenuation consists of 30-dB full-range attenuation due to air-to-body transmission loss and 10 dB/octave spectral decay due to a sound propagation loss within the body. These characteristics agree with the spectral characteristics of measured NAM signals. The sensors have a sensitivity of between 41 and 58 dB [V/Pa] at I kHz, and the mean signal-to-noise ratio of the detected signals was 15 dB. On the basis of these investigations, three types of silent-speech enhancement systems were developed: (1) simple, direct amplification of weak vocal-tract resonance signals using a wired urethane-elastomer NAM microphone, (2) simple, direct amplification using a wireless urethane-elastomer-duplex NAM microphone, and (3) transformation of the weak vocal-tract resonance signals sensed by a soft-silicone NAM microphone into whispered speech using statistical conversion. Field testing of the systems showed that they enable voice impaired people to communicate verbally using body-conducted vocal-tract resonance signals. Listening tests demonstrated that weak body-conducted vocal-tract resonance sounds can be transformed into intelligible whispered speech sounds. Using these systems, people with voice impairments can re-acquire speech communication with less effort. (C) 2009 Elsevier B.V. All rights reserved.ArticleSPEECH COMMUNICATION. 52(4):301-313 (2010)journal articl

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

Multisensory Integration Sites Identified by Perception of Spatial Wavelet Filtered Visual Speech Gesture Information

Perception of speech is improved when presentation of the audio signal is accompanied by concordant visual speech gesture information. This enhancement is most prevalent when the audio signal is degraded. One potential means by which the brain affords perceptual enhancement is thought to be through the integration of concordant information from multiple sensory channels in a common site of convergence, multisensory integration (MSI) sites. Some studies have identified potential sites in the superior temporal gyrus/sulcus (STG/S) that are responsive to multisensory information from the auditory speech signal and visual speech movement. One limitation of these studies is that they do not control for activity resulting from attentional modulation cued by such things as visual information signaling the onsets and offsets of the acoustic speech signal, as well as activity resulting from MSI of properties of the auditory speech signal with aspects of gross visual motion that are not specific to place of articulation information. This fMRI experiment uses spatial wavelet bandpass filtered Japanese sentences presented with background multispeaker audio noise to discern brain activity reflecting MSI induced by auditory and visual correspondence of place of articulation information that controls for activity resulting from the above-mentioned factors. The experiment consists of a low-frequency (LF) filtered condition containing gross visual motion of the lips, jaw, and head without specific place of articulation information, a midfrequency (MF) filtered condition containing place of articulation information, and an unfiltered (UF) condition. Sites of MSI selectively induced by auditory and visual correspondence of place of articulation information were determined by the presence of activity for both the MF and UF conditions relative to the LF condition. Based on these criteria, sites of MSI were found predominantly in the left middle temporal gyrus (MTG), and the left STG/S (including the auditory cortex). By controlling for additional factors that could also induce greater activity resulting from visual motion information, this study identifies potential MSI sites that we believe are involved with improved speech perception intelligibility

Early and Late Stage Mechanisms for Vocalization Processing in the Human Auditory System

The human auditory system is able to rapidly process incoming acoustic information, actively filtering, categorizing, or suppressing different elements of the incoming acoustic stream. Vocalizations produced by other humans (conspecifics) likely represent the most ethologically-relevant sounds encountered by hearing individuals. Subtle acoustic characteristics of these vocalizations aid in determining the identity, emotional state, health, intent, etc. of the producer. The ability to assess vocalizations is likely subserved by a specialized network of structures and functional connections that are optimized for this stimulus class. Early elements of this network would show sensitivity to the most basic acoustic features of these sounds; later elements may show categorically-selective response patterns that represent high-level semantic organization of different classes of vocalizations. A combination of functional magnetic resonance imaging and electrophysiological studies were performed to investigate and describe some of the earlier and later stage mechanisms of conspecific vocalization processing in human auditory cortices. Using fMRI, cortical representations of harmonic signal content were found along the middle superior temporal gyri between primary auditory cortices along Heschl\u27s gyri and the superior temporal sulci, higher-order auditory regions. Additionally, electrophysiological findings also demonstrated a parametric response profile to harmonic signal content. Utilizing a novel class of vocalizations, human-mimicked versions of animal vocalizations, we demonstrated the presence of a left-lateralized cortical vocalization processing hierarchy to conspecific vocalizations, contrary to previous findings describing similar bilateral networks. This hierarchy originated near primary auditory cortices and was further supported by auditory evoked potential data that suggests differential temporal processing dynamics of conspecific human vocalizations versus those produced by other species. Taken together, these results suggest that there are auditory cortical networks that are highly optimized for processing utterances produced by the human vocal tract. Understanding the function and structure of these networks will be critical for advancing the development of novel communicative therapies and the design of future assistive hearing devices

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Silent Speech Interfaces for Speech Restoration: A Review

This work was supported in part by the Agencia Estatal de Investigacion (AEI) under Grant PID2019-108040RB-C22/AEI/10.13039/501100011033. The work of Jose A. Gonzalez-Lopez was supported in part by the Spanish Ministry of Science, Innovation and Universities under Juan de la Cierva-Incorporation Fellowship (IJCI-2017-32926).This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.Agencia Estatal de Investigacion (AEI) PID2019-108040RB-C22/AEI/10.13039/501100011033Spanish Ministry of Science, Innovation and Universities under Juan de la Cierva-Incorporation Fellowship IJCI-2017-3292

Direct Speech Reconstruction From Articulatory Sensor Data by Machine Learning

This paper describes a technique that generates speech acoustics from articulator movements. Our motivation is to help people who can no longer speak following laryngectomy, a procedure that is carried out tens of thousands of times per year in the Western world. Our method for sensing articulator movement, permanent magnetic articulography, relies on small, unobtrusive magnets attached to the lips and tongue. Changes in magnetic field caused by magnet movements are sensed and form the input to a process that is trained to estimate speech acoustics. In the experiments reported here this “Direct Synthesis” technique is developed for normal speakers, with glued-on magnets, allowing us to train with parallel sensor and acoustic data. We describe three machine learning techniques for this task, based on Gaussian mixture models, deep neural networks, and recurrent neural networks (RNNs). We evaluate our techniques with objective acoustic distortion measures and subjective listening tests over spoken sentences read from novels (the CMU Arctic corpus). Our results show that the best performing technique is a bidirectional RNN (BiRNN), which employs both past and future contexts to predict the acoustics from the sensor data. BiRNNs are not suitable for synthesis in real time but fixed-lag RNNs give similar results and, because they only look a little way into the future, overcome this problem. Listening tests show that the speech produced by this method has a natural quality that preserves the identity of the speaker. Furthermore, we obtain up to 92% intelligibility on the challenging CMU Arctic material. To our knowledge, these are the best results obtained for a silent-speech system without a restricted vocabulary and with an unobtrusive device that delivers audio in close to real time. This work promises to lead to a technology that truly will give people whose larynx has been removed their voices back