Abstract: A method and an apparatus for audio signal processing evaluation are provided. The audio signal processing is performed on a synthesized audio signal to generate a processed audio signal. The synthesized audio signal is generated by adding a secondary signal into a master signal. The master signal is merely a speech signal. The signal processing is related to removing the secondary signal from the synthesized audio signal. The sound characteristics of the processed audio signal and the master signal are obtained, respectively. The sound characteristics include text content, and the text content is generated by performing speech-to-text on the processed audio signal and the master signal. The audio signal processing is evaluated according to the compared result between the sound characteristics of the processed audio signal and the master signal. The compared result includes the correctness of the text content of the processed audio signal relative to the master signal.

Abstract: A method for identifying and ranking potentially privileged documents using a machine learning topic model may include receiving a set of documents. The method may also include, for each of two or more documents in the set of documents, extracting a set of spans from the document, generating, using a machine learning topic model, a set of topics and a subset of legal topics for the set of spans, generating a vector of probabilities for each span with a probability being assigned to each topic in the set of topics for the span, assigning a score to one or more spans in the set of spans by summing the probabilities in the vector that are assigned to a topic in the subset of legal topics, and assigning a score to the document. The method may further include ranking the two or more documents by their assigned scores.

Abstract: Example methods and apparatus for providing voice alignment are described. One example method including: obtaining an original voice and a test voice, the test voice is a voice generated after the original voice is transmitted over a communications network; performing loss detection and/or discontinuity detection on the test voice, the loss detection is used to determine whether the test voice has a voice loss compared with the original voice, and the discontinuity detection is used to determine whether the test voice has voice discontinuity compared with the original voice; and aligning the test voice with the original voice based on a result of the loss detection and/or the discontinuity detection, to obtain an aligned original voice and an aligned test voice, the result of the loss detection and/or the discontinuity detection is used to indicate a manner of aligning the test voice with the original voice.

Abstract: In methods for training a natural language generation (NLG) model using a processor a document-level machine translation (MT) model is provided by training an MT model to receive as input, token sequences in a first language, and to generate as output, token sequences in a second language. An augmented document-level MT model is provided by training the document-level MT model to receive as input, paired language-independent structured data and token sequences in the first language, and to generate as output, token sequences in the second language. The augmented document-level MT model is trained to receive as input, language-independent structured data, and to generate as output, token sequences in the second language.

Abstract: Communication terminal includes a first microphone system, a second microphone system, and a noise reduction processing unit (NRPU). The NRPU receives a primary signal from the first microphone system and a secondary signal from the second microphone system. The NRPU dynamically identify an optimal transfer function of a correction filter which can be applied to the secondary signal provided by the second microphone system to obtain a correction signal. The correction signal is subtracted from the primary signal to obtain a remainder signal which approximates a signal of interest contained within the primary signal.

Abstract: An electronic device is provided. The electronic device includes a memory and a processor. The processor is configured to, based on acquiring a first sentence in a first language, determine whether to correct the first sentence to another sentence in the first language by using a second language model trained based on a learning corpus, and based on determining to correct the first sentence to another sentence in the first language, input the first sentence into a conversion model trained to acquire another sentence having a similarity greater than or equal to a threshold value to an input sentence and acquire a second sentence in the first language which is a corrected form of the first sentence, and based on acquiring the second sentence, input the second sentence into a translation model trained based on the learning corpus and acquire a third sentence in a second language.

Abstract: According to one embodiment, a recognition device includes storage and a processor. The storage is configured to store a first recognition model, a first data set, and tags, for each first recognition model. The processor is configured to acquire a second data set, execute recognition processing of the second recognition target data in the second data set by using the first recognition model, extract a significant tag of the tags stored in the storage in association with the first recognition model, based on the recognition processing result and the second correct data in the second data set, and create a second recognition model based on the acquired second data set and the first data set stored in the storage in association with the extracted tag.

Abstract: A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.

Abstract: Method and apparatus for adapting regular expressions for different contexts. Embodiments include identifying a regular expression in an initial form provided by a user. Embodiments include retrieving, from a repository, an adapted form of the regular expression based on the initial form. Embodiments include transforming the regular expression based on the adapted form to generate an adapted regular expression. Embodiments include evaluating the adapted regular expression to produce an output.

Abstract: Provided is technology for assessing whether uttered speech detected from input speech is speech suited to a prescribed purpose. A method comprises detecting, from input speech including speech uttered by a speaker and noise, the uttered speech corresponding to the speech uttered by the speaker, extracting an acoustic feature of the uttered speech, generating, from the uttered speech, a speech recognition result set with a recognition score, generating, from the speech recognition result set with the recognition score, a speech recognition result word vector expression set and a speech recognition result part-of-speech vector expression set, generating a target utterance estimation model, providing, using the target utterance estimation model, a probability of the uttered speech being suited to the prescribed purpose, and outputting the uttered speech and the speech recognition result set with the recognition score, the the uttered speech suitable to the prescribed purpose.

Abstract: Dynamic interfacing with applications is provided. For example, a system receives a first input audio signal. The system processes, via a natural language processing technique, the first input audio signal to identify an application. The system activates the application for execution on the client computing device. The application declares a function the application is configured to perform. The system modifies the natural language processing technique responsive to the function declared by the application. The system receives a second input audio signal. The system processes, via the modified natural language processing technique, the second input audio signal to detect one or more parameters. The system determines that the one or more parameters are compatible for input into an input field of the application. The system generates an action data structure for the application. The system inputs the action data structure into the application, which executes the action data structure.

Abstract: The disclosure may provide a method for obtaining a document layout, an electronic device, and a storage medium. The method may include: obtaining a plurality of pieces of first sample data; extracting structured information from each of the plurality of pieces of first sample data as target structured information corresponding to each of the plurality of pieces of first sample data; inputting the plurality of pieces of first sample data into an initial text generation model to generate predicted structured information corresponding to each of the plurality of pieces of first sample data; generating a first loss value based on a difference between the predicted structured information corresponding to each of the plurality of pieces of first sample data and the corresponding target structured information; and training a phrase generation ability of the initial text generation model based on the first loss value to generate the text generation model.

Abstract: The present disclosure relates to concurrent learning of a relationship estimation model and a phrase generation model. The relationship estimation model estimates a relationship between phrases. The phrase generation model generates a phrase that relates to an input phrase. The phrase generation model includes an encoder and a decoder. The encoder converts a phrase into a vector using a three-piece set as learning data. The decoder generates, based on the converted vector and a connection expression or a relationship label, a phrase having a relationship expressed by the connection expression or the relationship label for the phrase. The relationship estimation model generates a relationship score from the converted vector, which indicates each phrase included in a combination of the phrases, and a vector indicating the connection expression and the relationship label.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag. The high frequency regeneration is performed as a post-processing operation with a delay of 3010 samples per audio channel.

Abstract: With correct emotion classes selected as correct values of an emotion of an utterer of a first utterance from among a plurality of emotion classes C1, . . . , CK by listeners who have listened to the first utterance, as an input, the numbers of times ni that emotion classes Ci have been selected as the correct emotion classes are obtained, and rates of the numbers of times nk to a sum total of the numbers of times n1, . . . , nK or smoothed values of the rates are obtained as correct emotion soft labels tk(s) corresponding to the first utterance.

Abstract: A method and an electronic device for detecting crosstalk data are provided. The method for detecting crosstalk data can detect whether an audio data stream includes crosstalk data. The method includes: receiving a first audio data block, a second audio data block, and a reference time difference, wherein the first audio data block and the second audio data block separately include a plurality of audio data segments; using a time difference between an acquisition time of an audio data segment in the first audio data block and a corresponding audio data segment in the second audio data block as an audio segment time difference; and determining that the audio data segment of the first audio data block includes crosstalk data when the audio segment time difference does not match the reference time difference.

Abstract: An information processing device according to the present invention includes: a memory; and a processor coupled to the memory. The processor performs operations. The operations includes: generating, based on language data, a predicate argument structure including a predicate and an argument being an object of the predicate; generating first data indicating co-occurrence of the predicate and the argument in the predicate argument structure; decomposing the first data into a plurality of pieces of second data including fewer elements than elements included in the first data, and generating, based on the second data, third data including potential co-occurrence of the predicate and the argument; selecting the predicate argument structure by using the first data and the third data, and calculating, by using the third data, a score for a pair of the predicate argument structures including the selected predicate argument structure; and selecting the pair, based on the score.

Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for processing a semantic representation model, and relates to the field of artificial intelligence technologies. A specific implementation solution is: collecting a training corpus set including a plurality of training corpuses; training the semantic representation model using the training corpus set based on at least one of lexicon, grammar and semantics. In the present disclosure, by building the unsupervised or weakly-supervised training task at three different levels, namely, lexicon, grammar and semantics, the semantic representation model is enabled to learn knowledge at levels of lexicon, grammar and semantics from massive data, enhance the capability of universal semantic representation and improve the processing effect of the NLP task.

Abstract: Dynamic interfacing with applications is provided. For example, a system receives a first input audio signal. The system processes, via a natural language processing technique, the first input audio signal to identify an application. The system activates the application for execution on the client computing device. The application declares a function the application is configured to perform. The system modifies the natural language processing technique responsive to the function declared by the application. The system receives a second input audio signal. The system processes, via the modified natural language processing technique, the second input audio signal to detect one or more parameters. The system determines that the one or more parameters are compatible for input into an input field of the application. The system generates an action data structure for the application. The system inputs the action data structure into the application, which executes the action data structure.

Abstract: A method, system, and non-transitory processor-readable storage medium for automatic key phrase rule generation for automatic key phrase extraction including: receiving a corpus sample including a plurality of documents containing text, receiving a plurality of identified key phrases which relate to a topic of the text of at least one corresponding document; assigning a part-of-speech to each word in the corpus sample; generating a part-of-speech pattern from each identified key phrase; and generating key phrase rules.