Abstract: One provides (101) a plurality of frames of sampled audio content and then processes (102) that plurality of frames using a speech recognition search process that comprises, at least in part, determining whether to search each subword boundary contained within each frame on a frame-by-frame basis. These teachings will also readily accommodate determining whether to search each word boundary contained within each frame on a frame-by-frame basis.

Abstract: A method and apparatus for performing a voice search in a mobile communication device is disclosed. The method may include receiving a search query from a user of the mobile communication device, converting speech parts in the search query into linguistic representations, comparing the query linguistic representations to the linguistic representations of all items in the voice search database to find matches, wherein the voice search database has indexed all items that are associated with the device, displaying the matches to the user, receiving the user's selection from the displayed matches, and retrieving and executing the user's selection.

Abstract: A system includes a first communications device [105] to participate in a conversation with at least a second communication device [110]. An intelligent communication agent [120] monitors the conversation for at least one keyword. In response to detecting the at least one keyword, the intelligent communication agent performs a search for multimedia content corresponding to the at least one keyword and retrieves the multimedia content. A logic engine [135] determines relevant content of the multimedia content based on at least one of a conversation profile and at least one user profile for at least one of a user of the first communication device and at least a second user of the at least a second communication device. A transmission element [130] transmits the relevant content to at least one of the first communication device, the at least a second communication device, and a predetermined multimedia device [145].

Abstract: One provides (101) a plurality of frames of sampled audio content and then processes (102) that plurality of frames using a speech recognition search process that comprises, at least in part, searching for at least two of state boundaries, subword boundaries, and word boundaries using different search resolutions.

Abstract: A method (10) and system (200) for personalized voice dialogue can include tracking (12) a user's use of voice dialogue states or transitions and progressively offering (16) a user more efficient voice dialogue transitions or states such as voice dialogue transition or states having fewer and fewer words. The tracking of dialog states or transitions can include tracking (14) of repeated use of the dialogue states or transitions. A user can be prompted to create a new transition or state. The prompting (18) and confirmation and verification (20) by the user of a new transition or state can be done using SCXML language. The method can further include instantiating (21) the new transition or state with voice tags or words and performing (22) speech recognition using the new transition or state. The method can again determine (23) if the new transition or state is a repeat transition or state.

Abstract: A voice sample characterization front-end suitable for use in a distributed speech recognition context. A digitized voice sample 31 is split between a low frequency path 32 and a high frequency path 33. Both paths are used to determine spectral content suitable for use when determining speech recognition parameters (such as cepstral coefficients) that characterize the speech sample for recognition purposes. The low frequency path 32 has a thorough noise reduction capability. In one embodiment, the results of this noise reduction are used by the high frequency path 33 to aid in de-noising without requiring the same level of resource capacity as used by the low frequency path 32.

Abstract: A voice sample characterization front-end suitable for use in a distributed speech recognition context. A digitized voice sample 31 is split between a low frequency path 32 and a high frequency path 33. Both paths are used to determine spectral content suitable for use when determining speech recognition parameters (such as cepstral coefficients) that characterize the speech sample for recognition purposes. The low frequency path 32 has a thorough noise reduction capability. In one embodiment, the results of this noise reduction are used by the high frequency path 33 to aid in de-noising without requiring the same level of resource capacity as used by the low frequency path 32.

Abstract: An audio filter consists of two substantially identical stages with different purposes. The first stage (301) whitens detected noise, while preserving speech or other audible information in an undistorted manner. The second stage (303) effectively eliminates the residual white noise. Each stage, in one embodiment, includes a Mel domain based error minimization stage (108). A two stage Mel-frequency domain Wiener filter (300) is designed for each speech time frame in the Mel-frequency domain, instead of linear frequency domain. Each Mel domain based error minimization stage (108) minimizes the perceptual distortion and reduces the computation requirement to provide suitably filtered audible information.

Abstract: A system for enhancing the signal-to-noise ratio of a speech signal is avoided. A plurality of local energy maximums associated with a speech signal are determined. Presumably, each of these local energy maximums defines a speech pitch period. Typically, human pitch periods are approximately 100-400 Hz depending on the sex and age of the speaker. Because human speech typically includes more energy near the beginning of a pitch period than at the end of the pitch period, and background noise tends to remain relatively constant throughout the pitch period, the speech signal may be enhanced by increasing the energy associated with the beginning of the pitch period and/or by decreasing the energy associated with the end of the pitch period. Preferably, the amount of energy increase in the earlier portion of the pitch period is approximately equal to the amount of energy reduction in the later portion of the pitch period. In this manner, the total energy remains the constant.

Abstract: A system for enhancing the signal-to-noise ratio of a speech signal is disclosed. A plurality of local energy maximums associated with a speech signal are determined. Presumably, each of these local energy maximums defines a speech pitch period. Typically, human pitch periods are approximately 100-400 Hz depending on the sex and age of the speaker. Because human speech typically includes more energy near the beginning of a pitch period than at the end of the pitch period, and background noise tends to remain relatively constant throughout the pitch period, the speech signal may be enhanced by increasing the energy associated with the beginning of the pitch period and/or by decreasing the energy associated with the end of the pitch period. Preferably, the amount of energy increase in the earlier portion of the pitch period is approximately equal to the amount of energy reduction in the later portion of the pitch period. In this manner, the total energy remains the constant.

Abstract: A radio communication system includes a voice recognition system (218), a transmitter (202) and a processing system (210). The voice recognition system is utilized for receiving caller initiated messages, and the transmitter is used for transmitting messages to a plurality of SCRs (selective call radios) (122) of the radio communication system. The processing system, which is coupled to the voice recognition system, and the transmitter, is adapted to cause the voice recognition system to convert a voice signal representative of a voice message originated by a caller of the radio communication system to a text message (401, 417), wherein the text message is intended for a SCR, to then generate a likelihood of success that the voice signal has been flawlessly converted to a text message, to have a human listen to an audible representation of the voice signal, and to cause the transmitter to transmit the text message to the SCR (432).

Abstract: A selective call communication system (100) has a speech recognition system using an acoustic space (400) which has a plurality of probability density functions (pdfs). The selective call communication system (100) has an acoustic space generator (136) for representing speech in the acoustic space (400) which has a plurality of regions (1-14) having a subset of the plurality of probability density functions (502-516). The selective call communication system (100) has a tree generator (138) for generating a hierarchical tree structure (500) representing the subset of the plurality of probability density functions (502-516) associated with the plurality of regions (1-14), a score computer (132) for determining a region of the plurality of regions (1-14) indicative of a minimum distance to a center of the region for a speech sample received, and a speech recognizer (130) for calculating the probability density functions of the region for recognizing the speech sample received.