Abstract: Examples for augmenting user recognition via speech are provided. One example method comprises, on a computing device, monitoring a use environment via one or more sensors including an acoustic sensor, detecting utterance of a key phrase via data from the acoustic sensor, and based upon the selected data from the acoustic sensor and also on other environmental sensor data collected at different times than the selected data from the acoustic sensor, determining a probability that the key phrase was spoken by an identified user. The method further includes, if the probability meets or exceeds a threshold probability, then performing an action on the computing device.

Abstract: A device including a speech recognition function which recognizes speech from a user, includes: a loudspeaker which outputs speech to a space; a microphone which collects speech in the space; a first speech recognition unit which recognizes the speech collected by the microphone; a command control unit which issues a command for controlling the device, based on the speech recognized by the first speech recognition unit; and a control unit which prohibits the command issuance unit from issuing the command, based on the speech to be output from the loudspeaker.

Abstract: A method, a device, and a non-transitory storage medium are described in which a power of late reverberation of a speech signal is estimated based on early samples of the speech signal. The power of the late reverberation may be subtracted linearly or non-linearly from the speech signal.

Abstract: A system for enhancement of noisy speech comprises an input unit is configured to subdivide the spectrum of the input signal into a plurality of frequency sub-bands and to provide time-frequency coefficients X(k,m) for a sequence [X(k,m??D+1) . . . X(k,m?)] of observable noisy signal samples for each of said frequency sub-bands, where k and m are frequency and time indices, respectively, and D is larger than 1. The system further comprises enhancement processing unit configured to receive X(k,m) and to provide enhanced time-frequency coefficients ?(k,m), a storage for statistical model(s) of speech and for statistical model(s) of noise, and an optimizing unit configured to provide said enhanced time-frequency coefficients ?(k,m) using said statistical model of speech and said statistical model of noise, while considering said sequence [X(k,m??D+1) . . . X(k,m?)] of observable noisy signal samples.

Abstract: A computing device may compare a voice command to a customized voiceprint of a user. The computing device may, if a result of the comparison exceeds a threshold, determine the voice command matches the voiceprint, determine a security level associated with the voice command, generate a signal comprising an audible announcement, access website related information, and utilize customized user settings.

Abstract: An apparatus for processing an audio signal including a sequence of blocks of spectral values, includes: a processor for calculating an aliasing-affected signal using at least one first modification value for a first block of the sequence of blocks and using at least one different second modification value for a second block of the sequence of blocks and for estimating an aliasing-error signal representing an aliasing-error in the aliasing-affected signal; and a combiner for combining the aliasing-affected signal and the aliasing-error signal such that a processed signal obtained by the combining is an aliasing-reduced or aliasing-free signal.

Abstract: The application relates to a terminal and method for voice control on a terminal. A terminal according to some embodiments of the application includes: one or more processors, and a memory, wherein, the memory stores therein one or more computer readable program codes, and the processor or processors are configured to execute the one or more computer readable program codes, to match voice information in a voice instruction with preset voice information in the terminal upon reception of the voice instruction comprising the voice information and instruction information, to perform an operation corresponding to the instruction information upon determining successful matching, and to reject the operation corresponding to the instruction information upon determining unsuccessful matching.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: An improved gain-shape vector quantization is achieved by determining a number of bits to be allocated to a gain adjustment- and shape-quantizer for a plurality of combinations of a current bit rate and a first signal property. The bit allocation is derived by using an average of optimal bit allocations for a training data set. A number of bits to the gain adjustment and the shape quantizers for a plurality of combinations of the bit rate and a first signal are pre-calculated, and a table indicating the number of bits to be allocated to the gain adjustment- and the shape-quantizers for a plurality of combinations of the bit rate and a first signal property is created. In this way, the table can be used for achieving an improved bit allocation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for designating certain voice commands as hotwords. The methods, systems, and apparatus include actions of receiving a hotword followed by a voice command. Additional actions include determining that the voice command satisfies one or more predetermined criteria associated with designating the voice command as a hotword, where a voice command that is designated as a hotword is treated as a voice input regardless of whether the voice command is preceded by another hotword. Further actions include, in response to determining that the voice command satisfies one or more predetermined criteria associated with designating the voice command as a hotword, designating the voice command as a hotword.

Abstract: Typical speech recognition systems usually use speaker-specific speech data to apply speaker adaptation to models and parameters associated with the speech recognition system. Given that speaker-specific speech data may not be available to the speech recognition system, information indicative of language skills is employed in adapting configurations of a speech recognition system. According to at least one example embodiment, a method and corresponding apparatus, for speech recognition comprise maintaining information indicative of language skills of users of the speech recognition system. A configuration of the speech recognition system for a user is determined based at least in part on corresponding information indicative of language skills of the user. Upon receiving speech data from the user, the configuration of the speech recognition system determined is employed in performing speech recognition.

Abstract: In general, according to one embodiment, an electronic apparatus includes a sound source separation processor and an audio controller. The sound source separation processor is configured to perform a sound source separation function that separates an input audio signal into a voice signal and a background sound signal and emphasizes either the voice signal or the background sound signal. The audio controller is configured to control, based on scene information relating to a scene included in video, performance of the sound source separation function during display of the scene.

Abstract: Recording audio information from a meeting includes determining which audio input audio device (smartphones) correspond to which meeting participant, measuring volume levels in response to each of the meeting participants speaking, identifying one of the participants is speaking based on stored voice profiles and/or relative volume levels at each of the smartphones, recording on a first channel audio input at a first smartphone corresponding to the speaker, identifying another one of the participants is speaking based on stored voice profiles and/or relative volume levels at each of the smartphones, recording on a second channel, separate from the first channel, audio input at a second smartphone corresponding to the other speaker, and merging the first and second channels to provide a storyboard that includes audio input from the channels and identification of speakers based on which specific ones of the channels contains the audio input.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosure provides a computerized framework for automatically generating chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. The disclosed systems and methods construct domain-specific word-graphs based on account activity from specific domains and generate word-patterns. New words obtained from the patterns in the graph are introduced to transform the regular response. The graph is then pruned using data-driven thresholds in order to avoid spurious transformations, and paragraph vectors are also utilized to assign relevance scores to generated patterns such that only the patterns that are contextually similar to the original response (generic/regular response) are used.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: An apparatus for processing an audio signal including a sequence of blocks of spectral values includes: a processor for processing the sequence of blocks using at least one modification values for a first block to obtain aliasing-reduced or aliasing-free first result signal in an overlap range and using at least one second different modification value for a second block of the sequence of blocks to obtain an aliasing-reduced or aliasing-free second result signal in the overlap range; and a combiner for combining the first result signal and the second result signal in the overlap range to obtain a processed signal for the overlap range.

Abstract: An interactive response system directs input to a software-based router, which is able to intelligently respond to the input by drawing on a combination of human agents, advanced recognition and expert systems. The system utilizes human “intent analysts” for purposes of interpreting customer input. Automated recognition subsystems are trained by coupling customer input with IA-selected intent corresponding to the input, using model-updating subsystems to develop the training information for the automated recognition subsystems.

Abstract: The embodiments herein achieve a picture based communication system. The system allows users option to select one or more pictures, and any associated attributes. The selection of one or more pictures, and any associated attributes is taken as input. The selected words and attributes are converted to a graph representation, and subsequently the graph representation is converted to a sentence in target language. The method further involves predicting new relations, words, and attributes for further selection by user.