Abstract: A mistranscription generated by a speech recognition system is identified. A received utterance is matched to a first utterance member within a set of known utterance members. The matching operation matches fewer than the first plural number of words in the received utterance and the received utterance varies in a first particular manner as compared to a first word in a first slot in the first utterance member. The received utterance is sent to a mistranscription analyzer component which increments evidence that the received utterance is evidence of a mistranscription. Once the incremented evidence for the mistranscription exceeds a threshold, future received utterances containing the mistranscription are treated as though the first word was recognized.

Abstract: A method controls a robot. One or more processors receive sensor readings from one or more sensors that are monitoring a human in real time, where the human is currently observing a robotic action by a robot, and where the robotic action is a physical movement performed by the robot. The processor(s) determine a cognitive state of the human while the human is observing the robotic action by the robot, and then adjust the robotic action being performed by the robot based on the cognitive state of the human.

Abstract: A touch detection system is provided. The touch detection system includes a hardware processor. The touch detection system further includes a touch input surface mounted using a set of torque sensors, where a location of a touch on the touch input surface is calculated by the hardware processor based on collective results from the set of torque sensors, the touch comprising a user input.

Abstract: A touch detection system is provided. The touch detection system includes a hardware processor. The touch detection system further includes a touch input surface mounted using a set of torque sensors, where a location of a touch on the touch input surface is calculated by the hardware processor based on collective results from the set of torque sensors, the touch comprising a user input.

Abstract: A mistranscription generated by a speech recognition system is identified. A received utterance is matched to a first utterance member within a set of known utterance members. The matching operation matches fewer than the first plural number of words in the received utterance and the received utterance varies in a first particular manner as compared to a first word in a first slot in the first utterance member. The received utterance is sent to a mistranscription analyzer component which increments evidence that the received utterance is evidence of a mistranscription. Once the incremented evidence for the mistranscription exceeds a threshold, future received utterances containing the mistranscription are treated as though the first word was recognized.

Abstract: A mistranscription generated by a speech recognition system is identified. A first class of utterance members is provided for use by the speech recognition system, each utterance class member consisting of a respective number of words. The first class is defined by a first common meaning and a first common system response if a class member of the first class is recognized. If the speech recognition system matches a received utterance to a first class member of the first class, the received utterance is sent to a mistranscription analyzer. The received utterance contains a mistranscription as compared to the first class member. Evidence is incremented by the mistranscription analyzer that the received utterance is evidence of the mistranscription of the first class member. If the incremented evidence for the mistranscription of the first class member exceeds a first threshold, a second class member is added to the first class of utterance members based on the mistranscription of the first class member.

Abstract: A mistranscription generated by a speech recognition system is identified. A set of known utterance members is provided for use by a speech recognition system. Each utterance member consists of a respective plural number of words. A received utterance is matched to a first utterance member within the set of known utterance members. The first utterance member is a closest matching utterance member and has a first plural number of words. The matching operation matches fewer than the first plural number of words in the received utterance and the received utterance varies in a first particular manner as compared to a first word in a first slot in the first utterance member. The received utterance is sent to a mistranscription analyzer component which increments evidence that the received utterance is evidence of a mistranscription.

Abstract: A mistranscription generated by a speech recognition system is identified. A set of known utterance members is provided for use by a speech recognition system. Each utterance member consists of a respective plural number of words. A received utterance is matched to a first utterance member within the set of known utterance members. The first utterance member is a closest matching utterance member and has a first plural number of words. The matching operation matches fewer than the first plural number of words in the received utterance and the received utterance varies in a first particular manner as compared to a first word in a first slot in the first utterance member. The received utterance is sent to a mistranscription analyzer component which increments evidence that the received utterance is evidence of a mistranscription.

Abstract: A mistranscription generated by a speech recognition system is identified. A first class of utterance members is provided for use by the speech recognition system, each utterance class member consisting of a respective number of words. The first class is defined by a first common meaning and a first common system response if a class member of the first class is recognized. If the speech recognition system matches a received utterance to a first class member of the first class, the received utterance is sent to a mistranscription analyzer. The received utterance contains a mistranscription as compared to the first class member. Evidence is incremented by the mistranscription analyzer that the received utterance is evidence of the mistranscription of the first class member. If the incremented evidence for the mistranscription of the first class member exceeds a first threshold, a second class member is added to the first class of utterance members based on the mistranscription of the first class member.

Abstract: A method controls a robot. One or more processors receive sensor readings from one or more sensors that are monitoring a human in real time, where the human is currently observing a robotic action by a robot, and where the robotic action is a physical movement performed by the robot. The processor(s) determine a cognitive state of the human while the human is observing the robotic action by the robot, and then adjust the robotic action being performed by the robot based on the cognitive state of the human.

Abstract: Methods, systems, and computer program products for normalizing the speaking volume of participants in meetings are provided herein.

Abstract: Methods, systems, and computer program products for normalizing the speaking volume of participants in meetings are provided herein.

Abstract: A method for improving the performance of a noise cancellation device, the method includes determining whether one or more noise making objects (NMO) are near an audible range of the noise cancellation device and receiving a signal from the one or more NMOs indicative of a kind of noise the one or more NMOs is generating. The method also includes selecting a specific noise cancellation model to reduce an expected noise in response to the received kind of noise the one or more NMOs is generating.

Abstract: A method for improving the performance of a noise cancellation device, the method includes determining whether one or more noise making objects (NMO) are near an audible range of the noise cancellation device and receiving a signal from the one or more NMOs indicative of a kind of noise the one or more NMOs is generating. The method also includes selecting a specific noise cancellation model to reduce an expected noise in response to the received kind of noise the one or more NMOs is generating.

Abstract: A system for regulating the volume and frequency content of audio producing devices, the system includes: one or more noise making objects (NMO) configured with individual sound control devices in electrical communication with a noise management server and one or more audio producing devices configured with individual sound control devices; wherein the sound control devices have electronic logic processing, storage, and communication capabilities; wherein the noise management server utilize the sound control devices to: determine whether the NMO are producing noise in the audible range of one or more audio producing devices; determine a noise characteristic of the one or more NMO; command the one or more NMO to send the noise characteristic to the one or more audio producing devices; and wherein the volume and frequency content of audio produced by the one or more audio producing devices is adjusted in response to the received noise characteristic.

Abstract: A system for regulating the volume and frequency content of audio producing devices, the system includes: one or more noise making objects (NMO) configured with individual sound control devices in electrical communication with a noise management server and one or more audio producing devices configured with individual sound control devices; wherein the sound control devices have electronic logic processing, storage, and communication capabilities; wherein the noise management server utilize the sound control devices to: determine whether the NMO are producing noise in the audible range of one or more audio producing devices; determine a noise characteristic of the one or more NMO; command the one or more NMO to send the noise characteristic to the one or more audio producing devices; and wherein the volume and frequency content of audio produced by the one or more audio producing devices is adjusted in response to the received noise characteristic.

Abstract: A testing arrangement provided for speech recognition systems in vehicles. Preferably included are a “mobile client” secured in the vehicle and driven around at a desired speed, an audio system and speaker which plays back a set of prerecorded utterances stored digitally in a computer arrangement such that the speech of a human being is simulated, transmission of the speech signal to a server, followed by speech recognition and signal-to-noise ratio (SNR) computation. Here, the acceptability of the vehicular speech recognition system is preferably determined via comparison with pre-specified standards of recognition accuracy and SNR values.

Abstract: A method (and system) which autonomously generates a cohesive script from a text database for creating a speech corpus for concatenative text-to-speech, and more particularly, which generates cohesive scripts having fluency and natural prosody that can be used to generate compact text-to-speech recordings that cover a plurality of phonetic events.

Abstract: A method, apparatus and a computer program product to generate an audible speech word that corresponds to text. The method includes providing a text word and, in response to the text word, processing pre-recorded speech segments that are derived from a plurality of speakers to selectively concatenate together speech segments based on at least one cost function to form audio data for generating an audible speech word that corresponds to the text word. A data structure is also provided for use in a concatenative text-to-speech system that includes a plurality of speech segments derived from a plurality of speakers, where each speech segment includes an associated attribute vector each of which is comprised of at least one attribute vector element that identifies the speaker from which the speech segment was derived.

Abstract: Examples of paralinguistic events (e.g., breaths, coughs, sighs, etc.) are recorded. A text-to-speech (“TTS”) engine may insert the examples into a stream of synthetic speech using, for example, markup. The synthetic speech may include a combination of normal text and paralinguistic text.