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D. Messing

dblp:67/3744 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Speech recognition and synthesis · 100%
Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Speech recognition and synthesis › speech coding
low-bit-rate speech coding
0.112006
Exploiting nonacoustic sensors for speech encoding · IEEE Trans. Speech Audio Process. 2006
Natural language and speech › Speech recognition and synthesis
speech coding
0.112006
Exploiting nonacoustic sensors for speech encoding · IEEE Trans. Speech Audio Process. 2006
Natural language and speech › Speech recognition and synthesis
speech enhancement
0.112006
Exploiting nonacoustic sensors for speech encoding · IEEE Trans. Speech Audio Process. 2006

Methods — techniques the papers use, named apart from their topics

skin vibration sensor · 0.1microwave radar · 0.1bone conduction sensor · 0.1MELPe coder · 0.1
YearPublicationVenuePosition
2006 Exploiting nonacoustic sensors for speech encoding
abstract
The intelligibility of speech transmitted through low-rate coders is severely degraded when high levels of acoustic noise are present in the acoustic environment. Recent advances in nonacoustic sensors, including microwave radar, skin vibration, and bone conduction sensors, provide the exciting possibility of both glottal excitation and, more generally, vocal tract measurements that are relatively immune to acoustic disturbances and can supplement the acoustic speech waveform. We are currently investigating methods of combining the output of these sensors for use in low-rate encoding according to their capability in representing specific speech characteristics in different frequency bands. Nonacoustic sensors have the ability to reveal certain speech attributes lost in the noisy acoustic signal; for example, low-energy consonant voice bars, nasality, and glottalized excitation. By fusing nonacoustic low-frequency and pitch content with acoustic-microphone content, we have achieved significant intelligibility performance gains using the DRT across a variety of environments over the government standard 2400-bps MELPe coder. By fusing quantized high-band 4-to-8-kHz speech, requiring only an additional 116 bps, we obtain further DRT performance gains by exploiting the ear's insensitivity to fine spectral detail in this frequency region.
Thomas F. Quatieri, Kevin Brady 0001, D. Messing, Joseph P. Campbell, William M. Campbell, Michael S. Brandstein, Clifford J. Weinstein, John D. Tardelli, Paul D. Gatewood
IEEE Trans. Speech Audio Process.3