249 research outputs found
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
Neural Speaker Embeddings for Ultrasound-based Silent Speech Interfaces
Articulatory-to-acoustic mapping seeks to reconstruct speech from a recording
of the articulatory movements, for example, an ultrasound video. Just like
speech signals, these recordings represent not only the linguistic content, but
are also highly specific to the actual speaker. Hence, due to the lack of
multi-speaker data sets, researchers have so far concentrated on
speaker-dependent modeling. Here, we present multi-speaker experiments using
the recently published TaL80 corpus. To model speaker characteristics, we
adjusted the x-vector framework popular in speech processing to operate with
ultrasound tongue videos. Next, we performed speaker recognition experiments
using 50 speakers from the corpus. Then, we created speaker embedding vectors
and evaluated them on the remaining speakers. Finally, we examined how the
embedding vector influences the accuracy of our ultrasound-to-speech conversion
network in a multi-speaker scenario. In the experiments we attained speaker
recognition error rates below 3%, and we also found that the embedding vectors
generalize nicely to unseen speakers. Our first attempt to apply them in a
multi-speaker silent speech framework brought about a marginal reduction in the
error rate of the spectral estimation step.Comment: 5 pages, 3 figures, 3 table
EMG-to-Speech: Direct Generation of Speech from Facial Electromyographic Signals
The general objective of this work is the design, implementation, improvement and evaluation of a system that uses surface electromyographic (EMG) signals and directly synthesizes an audible speech output: EMG-to-speech
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