Abstract: An exemplary noise reduction system and method processes a speech signal that is delivered in a noisy channel or with ambient noise. Some exemplary embodiments of the system and method use filters to extract speech information, and focus on a subset of harmonics that are least corrupted by noise. Some exemplary embodiments disregard signal harmonics with low signal-to-noise ratio(s), and disregard amplitude modulations that are inconsistent with speech. An exemplary system and method processes a signal that focuses on a subset of harmonics that are least corrupted by noise, disregards the signal harmonics with low signal-to-noise ratio(s), and disregards amplitude modulations that are inconsistent with speech.

Abstract: An exemplary noise reduction system and method processes a speech signal that is delivered in a noisy channel or with ambient noise. Some exemplary embodiments of the system and method use filters to extract speech information, and focus on a subset of harmonics that are least corrupted by noise. Some exemplary embodiments disregard signal harmonics with low signal-to-noise ratio(s), and disregard amplitude modulations that are inconsistent with speech. An exemplary system and method processes a signal that focuses on a subset of harmonics that are least corrupted by noise, disregards the signal harmonics with low signal-to-noise ratio(s), and disregards amplitude modulations that are inconsistent with speech.

Abstract: An exemplary noise reduction system and method processes a speech signal that is delivered in a noisy channel or with ambient noise. Some exemplary embodiments of the system and method use filters to extract speech information, and focus on a subset of harmonics that are least corrupted by noise. Some exemplary embodiments disregard signal harmonics with low signal-to-noise ratio(s), and disregard amplitude modulations that are inconsistent with speech. An exemplary system and method processes a signal that focuses on a subset of harmonics that are least corrupted by noise, disregards the signal harmonics with low signal-to-noise ratio(s), and disregards amplitude modulations that are inconsistent with speech.

Abstract: An exemplary noise reduction system and method processes a speech signal that is delivered in a noisy channel or with ambient noise. Some exemplary embodiments of the system and method use filters to extract speech information, and focus on a subset of harmonics that are least corrupted by noise. Some exemplary embodiments disregard signal harmonics with low signal-to-noise ratio(s), and disregard amplitude modulations that are inconsistent with speech. An exemplary system and method processes a signal that focuses on a subset of harmonics that are least corrupted by noise, disregards the signal harmonics with low signal-to-noise ratio(s), and disregards amplitude modulations that are inconsistent with speech.

Abstract: An exemplary noise reduction system and method processes a speech signal that is delivered in a noisy channel or with ambient noise. Some exemplary embodiments of the system and method use filters to extract speech information, and focus on a subset of harmonics that are least corrupted by noise. Some exemplary embodiments disregard signal harmonics with low signal-to-noise ratio(s), and disregard amplitude modulations that are inconsistent with speech. An exemplary system and method processes a signal that focuses on a subset of harmonics that are least corrupted by noise, disregards the signal harmonics with low signal-to-noise ratio(s), and disregards amplitude modulations that are inconsistent with speech.

Abstract: Speech recognition is performed in near-real-time and improved by exploiting events and event sequences, employing machine learning techniques including boosted classifiers, ensembles, detectors and cascades and using perceptual clusters. Speech recognition is also improved using tandem processing. An automatic punctuator injects punctuation into recognized text streams.

Abstract: A system and method for processing a speech signal delivered in a noisy channel or with ambient noise that focuses on a subset of harmonics that are least corrupted by noise, that disregards the signal harmonics with low signal-to-noise ratio(s), and that disregards amplitude modulations inconsistent with speech.

Abstract: Speech recognition is performed in near-real-time and improved by exploiting events and event sequences, employing machine learning techniques including boosted classifiers, ensembles, detectors and cascades and using perceptual clusters. Speech recognition is also improved using tandem processing. An automatic punctuator injects punctuation into recognized text streams.

Abstract: A swirler assembly. The swirler assembly may include a hub, a vane positioned on the hub, and a fuel supply passageway extending from the hub through the vane. The fuel supply passageway may include a substantially triangular shape.

Abstract: A swirler assembly. The swirler assembly may include a hub, a vane positioned on the hub, and a fuel supply passageway extending from the hub through the vane. The fuel supply passageway may include a substantially triangular shape.

Abstract: A machine readable symbol includes a data field of arranged data cells. Each data cell is assigned a data pattern value or a finder pattern value. Preferably, at least some of the data cells, or even all of the data cells, include at least one data pattern value and a finder pattern value which are overlapping. A color corresponding to the data pattern value, finder pattern value, or overlapping data pattern value and finder pattern value is assigned to each data cell. A finder pattern is discernable when the symbol is placed under a first color filter, or exposed to a first color, or the symbol image is processed by a first software color filter of an electronic device. A data pattern is discernable when the symbol is placed under a second color filter, or exposed to a second color, or the symbol image is processed by a second software color filter.

Abstract: A machine readable symbol includes a data field of arranged data cells. Each data cell is assigned a data pattern value or a finder pattern value. Preferably, at least some of the data cells, or even all of the data cells, include at least one data pattern value and a finder pattern value which are overlapping. A color corresponding to the data pattern value, finder pattern value, or overlapping data pattern value and finder pattern value is assigned to each data cell. A finder pattern is discernable when the symbol is placed under a first color filter, or exposed to a first color, or the symbol image is processed by a first software color filter of an electronic device. A data pattern is discernable when the symbol is placed under a second color filter, or exposed to a second color, or the symbol image is processed by a second software color filter.

Abstract: A machine readable symbol includes a rectilinear data field having a plurality of data cells arranged into a matrix. Each data cell is assigned a data pattern value or a finder pattern value. Preferably, at least some of the data cells, or even all of the data cells, include at least one data pattern value and a finder pattern value which are overlapping. A color corresponding to the data pattern value, finder pattern value, or overlapping data pattern value and finder pattern value is assigned to each data cell. A finder pattern is discernable when the symbol is placed under a first color filter or exposed to a first color. A data pattern is discernable when the symbol is placed under a second color filter, or exposed to a second color.