Noise Robust Vocoding at 2400 bps

Preuss, R.; Fabbri, Darren R.; Cruthirds, Daniel R.

doi:10.1109/icosp.2006.345512

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…A major focal point was the DARPA Advanced Speech Encoding Program (ASE) of the early 2000's, which funded research on low bit rate speech synthesis "with acceptable intelligibility, quality, and aural speaker recognizability in acoustically harsh environments", thus spurring developments in speech processing using a variety of mechanical and electromagnetic glottal activity sensors ;Tardelli Ed. (2004); Preuss et al (2006); Quatieri et al (2006)).…”

Section: Historical Frameworkmentioning

confidence: 99%

“…(2004); Quatieri et al (2006)). In Ng et al (2000), perfectly intelligible speech was obtained using a GEMS device (described below) from a signal with an initial signal to noise ratio of only 3 dB, while excellent results on noise robust vocoding in three harsh military noise environments using GEMS and PMIC (description below) are reported in Preuss et al (2006).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Analysis of glottal activity using electromagnetic (Titze et al (2000); Ng et al (2000); Tardelli Ed. (2004); Preuss et al (2006); Quatieri et al (2006)), or vibration (Bos and Tack (2005); Patil and Hansen (2009, this issue)) sensors; 5. Surface electromyography (sEMG) of the articulator muscles or the larynx (Jorgensen et al (2003); Maier-Hein et al (2005); ; Hasegawa-Johnson (2008)); Jorgensen and Dusan (2009, this issue); Schultz and Wand (2009, this issue); 6.…”

Section: Introductionmentioning

confidence: 99%

“…Physiological microphone, or PMIC. The PMIC(Bos and Tack (2005),Quatieri et al (2006);Preuss et al (2006);Patil and Hansen (2009, this issue)), is a more sophisticated form of the throat microphone. It is worn as a neckband, consisting of a piezoelectric sensor…”

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confidence: 99%

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Silent speech interfaces

Denby¹,

Schultz²,

Honda³

et al. 2010

Speech Communication

410

230

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International audienceThe possibility of speech processing in the absence of an intelligible acoustic signal has given rise to the idea of a 'silent speech' interface, to be used as an aid for the speech-handicapped, or as part of a communications system operating in silence-required or high-background-noise environments. The article first outlines the emergence of the silent speech interface from the fields of speech production, automatic speech processing, speech pathology research, and telecommunications privacy issues, and then follows with a presentation of demonstrator systems based on seven different types of technologies. A concluding section underlining some of the common challenges faced by silent speech interface researchers, and ideas for possible future directions, is also provided

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Section: Historical Frameworkmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Silent speech interfaces

Denby¹,

Schultz²,

Honda³

et al. 2010

Speech Communication

410

230

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show abstract

“…In particular, silent speech recognition systems (SSRSs) enable speech communication to be needed when an audible acoustic signal is unavailable [1]. In addition to "physical" SSRSs [2][3][4][5], in the "electrical" ones, articulation may be inferred from actuator muscle signals or predicted using command signals obtained directly from the brain. Especially, the latter could be speech prosthesis for individuals with severe communication impairments.…”

Section: Introductionmentioning

confidence: 99%

Decoding Silent Speech in Japanese from Single Trial EEGS: Preliminary Results

T¹,

S²

2015

J Comput Sci Syst Biol

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We propose a new scheme for speaker-dependent silent speech recognition systems (SSRSs) using both single-trial electroencephalograms (EEGs) scalp-recorded and speech signals measured during overtly and covertly speaking "janken" and "season" in Japanese. This scheme consists of two phases. The learning phase specifies a Kalman filter using spectrograms of the speech signals and independent components (ICs), whose equivalent current dipole source localization (ECDL) solutions were located mainly at the Broca's area, of the EEGs during the actual speech. In case of the "season" task, the speech signals were transformed into vowel and consonant sequences, and these relationships were learned by hidden Markov model (HMM) with Gaussian mixture densities. The decoding phase predicts spectrograms for the silent "janken" and "season" using the Kalman filter with the EEGs during the silent speech. For the silent "season", the predicted spectrograms were inputted to the HMM, and which "season" was silently spoken was determined by the maximal log-likelihood among each HMM. Our preliminary results as training steps are as follows: the silent "jankens" were correctly discriminated; the silent "season"-HMMs worked well, suggesting that this scheme might be applied to the discrimination between all the pairs of the hiraganas.

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