Abstract: A method includes receiving a corpus comprising a set of pre-segmented texts. The method further includes creating a plurality of modified pre-segmented texts for the set of pre-segmented texts by extracting a set of semantic terms for each pre-segmented text within the set of pre-segmented texts and applying at least one domain tag for each pre-segmented text within the set of pre-segmented texts. The method further includes clustering the plurality of modified pre-segmented texts into one or more conceptual units, wherein each of the one or more conceptual units is associated with one or more templates, wherein each of the one or more templates corresponds to one of the plurality of modified pre-segmented texts.

Abstract: Disclosed are a method and a device for expanding data of a bilingual corpus.

Abstract: A method for generating a notification by an electronic device to alert a user of the electronic device is disclosed. In this method, a speech phrase may be received. Then, the received speech phrase may be recognized, by a processor, as a command to generate the notification. In addition, one or more context data of the electronic device may be detected by at least one sensor. It may be determined whether the notification is to be generated at least based on the context data. The notification may be generated, by the processor, based on the context data and the command to generate the notification.

Abstract: Software that trains an artificial neural network for generating vector representations for natural language text, by performing the following steps: (i) receiving, by one or more processors, a set of natural language text; (ii) generating, by one or more processors, a set of first metadata for the set of natural language text, where the first metadata is generated using supervised learning method(s); (iii) generating, by one or more processors, a set of second metadata for the set of natural language text, where the second metadata is generated using unsupervised learning method(s); and (iv) training, by one or more processors, an artificial neural network adapted to generate vector representations for natural language text, where the training is based, at least in part, on the received natural language text, the generated set of first metadata, and the generated set of second metadata.

Abstract: In some embodiments, a method which includes receiving an indication of the presence of a first message from a first client device, the first message including a first specification file and a first network address associated with a first print data file. The method includes retrieving the first print data file from the first network address. The method includes processing the first specification file and the first print data file by a raster image processor.

Abstract: The technology described in this document can be embodied in a computer-implemented method that includes receiving, at a processing system, a first signal including an output of a speaker device and an additional audio signal. The method also includes determining, by the processing system, based at least in part on a model trained to identify the output of the speaker device, that the additional audio signal corresponds to an utterance of a user. The method further includes initiating a reduction in an audio output level of the speaker device based on determining that the additional audio signal corresponds to the utterance of the user.

Abstract: The present technology relates to a device, a method, and a program for expanding a frequency band, which are capable of obtaining high-quality sound with a small processing amount. A low band extraction band-pass filter processing unit passes a predetermined band of a low band of an input signal and generates a low band sub band signal. A band-pass filter calculation circuit calculates band-pass filter coefficients of band-pass filters having sub bands of high bands as a pass band based on an estimate value of high band sub band power, and an addition unit obtains one filter coefficient by adding the band-pass filter coefficients. A poly-phase configuration level adjustment filter performs up-sampling and level adjustment by performing filtering on a flattened signal obtained from a low band sub band signal using the filter coefficient obtained by the addition unit, and generates a high band signal. An addition unit obtains an output signal by adding the high band signal to the low band signal.

Abstract: A challenge of audio watermarking systems in which an acoustic path is involved is the robustness against microphone pickup in case of surrounding noise. The strength of phase-based watermarking is increased by determining a masking threshold for a current frequency bin in a frequency/phase representation changing the phase based on that masking threshold and an allowed phase change value, calculating an allowed magnitude change value for the current frequency bin and calculating from an audio quality level value a magnitude change scaling factor for the magnitude change value, and increasing its magnitude accordingly.

Abstract: Technology is disclosed that improves language coverage by selecting sentences to be used as training data for a language processing engine. The technology accomplishes the selection of a number of sentences by obtaining a group of sentences, computing a score for each sentence, sorting the sentences based on their scores, and selecting a number of sentences with the highest scores. The scores can be computed by dividing a sum of frequency values of unseen words (or n-grams) in the sentence by a length of the sentence. The frequency values can be based on posts in one or more particular domains, such as the public domain, the private domain, or other specialized domains.

Abstract: Methods, systems, and apparatus are described for inducing a user of a speech recognition system to adjust their own behavior. For example, in one implementation, a speech recognition system that allows children to control electronic devices can improve the child's speech development, by encouraging the child to speak more clearly. To do so, the speech recognition system can generate a phonetic representation of a term spoken by the child, and can determine whether the phonetic representation matches a particular canonical pronunciation of the particular term that is deemed age-appropriate for the child. Upon determining that the particular canonical pronunciation that matches the phonetic representation of the term spoken by the child is not age-appropriate, the speech recognition system can select and implement a variety of remediation strategies for inducing the child to repeat the term using a pronunciation that is considered age-appropriate.

Abstract: A frame generator 3 frames an input signal and generates a frame signal. A reference signal storage unit 7 stores therein a reference signal showing a periodic noise signal. A correlation value calculator 8 calculates a correlation value between the frame signal and the reference signal. A correlation candidate position decider 9 decides a plurality of correlation candidate positions serving as candidates for a signal portion of the reference signal correlated with the frame signal. A noise reduction processor 10 reduces a periodic noise signal, which is included in the frame signal, by using each signal portion of the plurality of correlation candidate positions, and generates a plurality of candidate output signals. An output signal decider 11 decides a candidate output signal, in which the periodic noise signal is reduced the most among the plurality of candidate output signals, and outputs the decided output signal.

Abstract: Methods, devices, and computer program products for recognizing and responding to natural language input are described herein. Natural language input is received at a natural language input interface of a computing device and transformed into computer-usable text. A natural language input recognizer obtains evidence from one or more evidence source and generates an evidence graph based on the evidence obtained. Evidence may be obtained asynchronously, and the natural language input recognizer may update the evidence graph upon receipt of additional evidence. The natural language input recognizer generates a set of recognition hypotheses based on the evidence graph and selects one of the recognition hypotheses as a recognition result for the natural language input. Semantic models, evidence models, and response models may be employed to generate the evidence graph and respond to the recognition result selected for the natural language input.

Abstract: A social networking system determines whether a particular user is qualified to provide translations of text from a first language to a second language. The determination may include evaluation of the language competencies of the user, and also of the trustworthiness of the user as a translator, as determined based on prior translations submitted by the user. The social networking system also selects translations of a text item for a user to whom that text is to be shown. When evaluating a candidate translation for presentation to the user, the evaluation may assess factors such as the determined qualification as a translator of the user who provided the candidate translation; a quality score of the candidate translation itself; and/or the similarity of the user viewing the content and the user providing the candidate translation.

Abstract: Technology is disclosed for creating and tuning classifiers for language dialects and for generating dialect-specific language modules. A computing device can receive an initial training data set as a current training data set. The selection process for the initial training data set can be achieved by receiving one or more initial content items, establishing dialect parameters of each of the initial content items, and sorting each of the initial content items into one or more dialect groups based on the established dialect parameters. The computing device can generate, based on the initial training data set, a dialect classifier configured to detect language dialects of content items to be classified. The computing device can augment the current training data set with additional training data by applying the dialect classifier to candidate content items. The computing device can then update the dialect classifier based on the augmented current training data set.

Abstract: Methods, systems, and apparatus are described for inducing a user of a speech recognition system to adjust their own behavior. For example, in one implementation, a speech recognition system that allows children to control electronic devices can improve the child's speech development, by encouraging the child to speak more clearly. To do so, the speech recognition system can generate a phonetic representation of a term spoken by the child, and can determine whether the phonetic representation matches a particular canonical pronunciation of the particular term that is deemed age-appropriate for the child. Upon determining that the particular canonical pronunciation that matches the phonetic representation of the term spoken by the child is not age-appropriate, the speech recognition system can select and implement a variety of remediation strategies for inducing the child to repeat the term using a pronunciation that is considered age-appropriate.

Abstract: A computer-implemented method performed by a data processing apparatus includes receiving an audio signal that includes a frequency-domain representation of an audio file, extracting, from the audio signal, a plurality of frequency-domain data values that correspond to at least a portion of the audio file, compressing the plurality of data values to form a compressed frequency domain value file, and transmitting the compressed frequency domain value file to a server to identify the audio file.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating language model biasing. In some implementations, context data is received. A likely context associated with a user is determined based on at least a portion of the context data. One or more language model biasing parameters based at least on the likely context associated with the user is selected. A context confidence score associated with the likely context based on at least a portion of the context data is determined. One or more language model biasing parameters based at least on the context confidence score is adjusted. A baseline language model based at least on the one or more of the adjusted language model biasing parameters is biased. The baseline language model is provided for use by an automated speech recognizer (ASR).

Abstract: In various example embodiments, a system and method for correction of user input are presented. In one embodiment, a method includes receiving a plurality of user strings, selecting one or more string pairs from the plurality of user strings based on a character operator difference between the first string and the second string being below a threshold number, filtering the one or more string pairs to generate a filtered set of strings pairs representing corrections, and correcting user input in a different session by replacing input that matches a first string in a filtered string pair with a second string in the filtered string pair.

Abstract: A method comprising receiving an audio input signal comprising speech, determining an accent class corresponding to the speech, identifying an accented phone pattern within the speech, replacing the accented phone pattern with an unaccented phone pattern, and generating an unaccented output signal from the unaccented phone pattern.

Abstract: A speech recognition system for resolving impaired utterances can have a speech recognition engine configured to receive a plurality of representations of an utterance and concurrently to determine a plurality of highest-likelihood transcription candidates corresponding to each respective representation of the utterance. The recognition system can also have a selector configured to determine a most-likely accurate transcription from among the transcription candidates. As but one example, the plurality of representations of the utterance can be acquired by a microphone array, and beamforming techniques can generate independent streams of the utterance across various look directions using output from the microphone array.