Abstract: Efficient empirical determination, computation, and use of an acoustic confusability measure comprises: (1) an empirically derived acoustic confusability measure, comprising a means for determining the acoustic confusability between any two textual phrases in a given language, where the measure of acoustic confusability is empirically derived from examples of the application of a specific speech recognition technology, where the procedure does not require access to the internal computational models of the speech recognition technology, and does not depend upon any particular internal structure or modeling technique, and where the procedure is based upon iterative improvement from an initial estimate; (2) techniques for efficient computation of empirically derived acoustic confusability measure, comprising means for efficient application of an acoustic confusability score, allowing practical application to very large-scale problems; and (3) a method for using acoustic confusability measures to make principled

Abstract: Methods, systems, and apparatus for receiving, by a voice action system, data specifying trigger terms that trigger an application to perform a voice action and a context that specifies a status of the application when the voice action can be triggered. The voice action system receives data defining a discoverability example for the voice action that comprises one or more of the trigger terms that trigger the application to perform the voice action when a status of the application satisfies the specified context. The voice action system receives a request for discoverability examples for the application from a user device having the application installed, and provides the data defining the discoverability examples to the user device in response to the request. The user device is configured to provide a notification of the one or more of the trigger terms when a status of the application satisfies the specified context.

Abstract: Techniques for improving machine learning and text classification are described. The described techniques include improved processes for collecting training data to train a machine classifier. Some data sets are very large but contain only a small number of positive or negative training examples. The described text classification system obtains training examples by intelligently identifying documents that are likely to present or identify positive or negative training examples. The text classification system employs these techniques to train a classifier to categorize patent claims according some legal rule, such as subject-matter eligibility requirements under patent law.

Abstract: Systems and methods for enhancing the depth and richness of content in computer-generated patent applications by providing non-explicit support for individual claim features are disclosed. Exemplary implementations may: receive a previously unseen claim feature, the previously unseen claim feature being absent from the previously received patent documents; provide one or more sentences of never-been-seen-before computer-generated text using the trained machine learning model and the previously unseen claim feature as input; and insert the one or more sentences of non-explicit support in a draft patent application proximal to explicit support for the previously unseen claim feature.

Abstract: An apparatus for selecting one of a first encoding algorithm and a second encoding algorithm includes a filter configured to receive the audio signal, to reduce the amplitude of harmonics in the audio signal and to output a filtered version of the audio signal. First and second estimators are provided for estimating first and second quality measures in the form of SNRs of segmented SNRs associated with the first and second encoding algorithms without actually encoding and decoding the portion of the audio signal using the first and second encoding algorithms. A controller is provided for selecting the first encoding algorithm or the second encoding algorithm based on a comparison between the first quality measure and the second quality measure.

Abstract: Transportation of audio data is provided between a first network element and a second network element. The first network element divides multichannel audio data by channel into a plurality of individual channels. The first network element packages each of the plurality of individual channels together with a corresponding sampling set of audio data into a single packet. The first network element transmits to the second network element each packet that includes a channel having an active sampling set of audio data.

Abstract: Disclosed are machine learning-based technologies that analyze an audio input and provide speaker state predictions in response to the audio input. The speaker state predictions can be selected and customized for each of a variety of different applications.

Abstract: A signal encoding method and device are disclosed. The method includes, when an encoding manner of a previous frame of a currently-input frame is a continuous encoding manner, predicting a comfort noise that is generated by a decoder according to the currently-input frame when the currently-input frame is encoded into an SID frame, determining an actual silence signal, determining a deviation degree between the comfort noise and the actual silence signal, determining an encoding manner of the currently-input frame according to the deviation degree, and encoding the currently-input frame according to the encoding manner of the currently-input frame. It is determined, according to the deviation degree between the comfort noise and the actual silence signal, that the encoding manner of the currently-input frame is the hangover frame encoding manner or the SID frame encoding manner, which can save communication bandwidth.

Abstract: Provided are a computer program product, system, and method for using remote words in data streams from remote devices to autocorrect input text. Remote words included in a at least one data stream are accessed from at least one remote device over a network to store in a remote word corpus. User input text is received in a text entry field in a user interface rendered by the computer device. Local words are maintained in a local word corpus stored in the computer device. The local words and the remote words are used to autocorrect and validate the user input text.

Abstract: A method includes performing, with at least one processing device, natural language generation using both (i) a semantic word and clause representation generated from syntactically-labeled context and (ii) a syntax generated from common semantic relations between sequential words and clauses. Performing the natural language generation includes pushing words onto a stack based on the semantic word and clause representation and based on words and clauses already on the stack and popping the words and clauses off the stack to be replaced by clauses based on the syntax. Pushing the words onto the stack could be based only on components of the semantic word and clause representation coming from future context.

Abstract: An audio encoder for encoding segments of coefficients, the segments of coefficients representing different time or frequency resolutions of a sampled audio signal, the audio encoder including a processor for deriving a coding context for a currently encoded coefficient of a current segment based on a previously encoded coefficient of a previous segment, the previously encoded coefficient representing a different time or frequency resolution than the currently encoded coefficient. The audio encoder further includes an entropy encoder for entropy encoding the current coefficient based on the coding context to obtain an encoded audio stream.

Abstract: A non-transitory processor-readable medium storing code representing instructions to be executed by a processor includes code to cause the processor to receive acoustic data representing an utterance spoken by a language learner in a non-native language in response to prompting the language learner to recite a word in the non-native language and receive a pronunciation lexicon of the word in the non-native language. The pronunciation lexicon includes at least one alternative pronunciation of the word based on a pronunciation lexicon of a native language of the language learner. The code causes the processor to generate an acoustic model of the at least one alternative pronunciation in the non-native language and identify a mispronunciation of the word in the utterance based on a comparison of the acoustic data with the acoustic model. The code causes the processor to send feedback related to the mispronunciation of the word to the language learner.

Abstract: An audio encoder for providing an encoded representation on the basis of an audio signal, wherein the audio encoder is configured to obtain a noise information describing a noise included in the audio signal, and wherein the audio encoder is configured to adaptively encode the audio signal in dependence on the noise information, such that encoding accuracy is higher for parts of the audio signal that are less affected by the noise included in the audio signal than for parts of the audio signal that are more affected by the noise included in the audio signal.

Abstract: A voice input unit has predetermined directivity for acquiring a voice. A sound source arrival direction estimation unit operating as a first direction detection unit detects a first direction, which is an arrival direction of a signal voice of a predetermined target, from the acquired voice. Moreover, a sound source arrival direction estimation unit operating as a second direction detection unit detects a second direction, which is an arrival direction of a noise voice, from the acquired voice. A sound source separation unit, a sound volume calculation unit, and a detection unit having an S/N ratio calculation unit detect a sound source separation direction or a sound source separation position, based on the first direction and the second direction.

Abstract: A voice processing system includes a voice acquisition unit that acquires an utterance of a user, a voice recognition unit that recognizes contents of the utterance acquired by the voice acquisition unit, an execution unit that executes processing based on a result of the voice recognition unit, and a determination unit that determines a candidate of a word to substitute a certain word. When the voice recognition unit recognizes a request including a first word, the execution unit executes the request including the first word and provides the user with a processing result. The determination unit determines a second word that is a substitute candidate of the first word. The execution unit also executes a request including the second word instead of the first word and stores a processing result in a storage unit.

Abstract: System and method for acoustic signal processing are disclosed. An exemplary device for acoustic signal processing includes a voice activity detector configured to detect a speech of a user. The device includes a microphone configured to receive acoustic signals from the user. The device further includes at least one processor configured to process the acoustic signals in response to detecting the speech of the user. The at least one processor is in an idle state before the speech of the user is detected.

Abstract: An audio encoder has a first information sink oriented encoding branch, a second information source or SNR oriented encoding branch, and a switch for switching between the first encoding branch and the second encoding branch, wherein the second encoding branch has a converter into a specific domain different from the spectral domain, and wherein the second encoding branch furthermore has a specific domain coding branch, and a specific spectral domain coding branch, and an additional switch for switching between the specific domain coding branch and the specific spectral domain coding branch. An audio decoder has a first domain decoder, a second domain decoder for decoding a signal, and a third domain decoder and two cascaded switches for switching between the decoders.

Abstract: A voice controlled room automation system that includes a speaker device situated in a guest room, a hotel automation controller operatively coupled to one or more components in the guest room, and a web service operatively coupled to the speaker device and the hotel automation controller. The web service is configured to receiving voice commands from the speaker device, process the voice command using speech recognition, interpret the voice command to determine a corresponding command for the hotel automation controller, and transmit the corresponding command to the hotel automation controller. The hotel automation controller is configured to receive the corresponding command from the web service, and to carry out the corresponding command by interacting with one or more of the components in the room. In some cases, the hotel automation controller may be configured to initiate an announcement or query on the speaker device via the web service.

Abstract: Coordinated audio and video filter pairs are applied to enhance artistic and emotional content of audiovisual performances. Such filter pairs, when applied in audio and video processing pipelines of an audiovisual application hosted on a portable computing device (such as a mobile phone or media player, a computing pad or tablet, a game controller or a personal digital assistant or book reader) can allow user selection of effects that enhance both audio and video coordinated therewith. Coordinated audio and video are captured, filtered and rendered at the portable computing device using camera and microphone interfaces, using digital signal processing software executable on a processor and using storage, speaker and display devices of, or interoperable with, the device. By providing audiovisual capture and personalization on an intimate handheld device, social interactions and postings of a type made popular by modern social networking platforms can now be extended to audiovisual content.

Abstract: Proposed is a method and apparatus for determining a weighting function for quantizing a linear predictive coding (LPC) coefficient and having a low complexity. The weighting function determination apparatus may convert an LPC coefficient of a mid-subframe of an input signal to one of a immittance spectral frequency (ISF) coefficient and a line spectral frequency (LSF) coefficient, and may determine a weighting function associated with an importance of the ISF coefficient or the LSF coefficient based on the converted ISF coefficient or LSF coefficient.