Abstract: A method includes obtaining a plurality of training data sets each associated with a respective native language and includes a plurality of respective training data samples. For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample.

Abstract: A framework ranks multiple hypotheses generated by one or more ASR engines for each input speech utterance. The framework jointly implements ASR improvement and NLU. It makes use of NLU related knowledge to facilitate the ranking of competing hypotheses, and outputs the top-ranked hypothesis as the improved ASR result together with the NLU results of the speech utterance. The NLU results include intent detection results and the slot filling results.

Abstract: The present disclosure provides a corpus cleaning method and a corpus entry system. The method includes: obtaining an input utterance; generating a predicted value of an information amount of each word in the input utterance according to the context of the input utterance using a pre-trained general model; and determining redundant words according to the predicted value of the information amount of each word, and determining whether to remove the redundant words from the input utterance. In such a manner, the objectivity and accuracy of corpus cleaning can be improved.

Abstract: Provided is an incorrect triple detection system including a triple selector configured to select a target triple (subject, type, object) in a knowledge base, a sampler configured to create a sentence model by connecting object triples sharing entities included in the target triple, a model builder configured to embed the sentence model into a vector space to create a training entity vector and build an embedding model, and an incorrect triple detector configured to detect an incorrect triple by inputting a test triple into the embedding model.

Abstract: In a frictionless handoff of audio content playing, a client device listens for ultrasonic audio. The client hears a playing of a modified audio content by another client device, which includes audio content and an ultrasonic audio quick response (QR) code overlaid on the audio content. The ultrasonic audio QR code includes location information corresponding to a location in the audio content. The client device extracts the ultrasonic audio QR code from the modified audio content. After determining that the playing of the modified audio content has stopped, the client device receives a command to resume playing of the audio content on the client device. In, response to the command, the client device retrieves location information in a last extracted ultrasonic audio QR code and plays the audio content starting at a location in the audio content corresponding to the location information in the last extracted ultrasonic audio QR code.

Abstract: Systems and methods are provided for language-based service hailing. Such system may comprise one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the computing system to obtain a plurality of speech samples, each speech sample comprising one or more words spoken in a language, train a neural network model with the speech samples to obtain a trained model for determining languages of speeches, obtain a voice input, identify at least one language corresponding to the voice based at least on applying the trained model to the voice input, and communicate a message in the identified language.

Abstract: The present disclosure relates to processing operations configured to provide, through an adapted user interface of an application/service, seamless application of autocorrection for an electronic document and provision of review insights into the autocorrections. In addition to applying autocorrections, processing described herein selectively determines when to apply an autocorrection (e.g., while a user is entering an input or after) and further highlights autocorrections, through a user interface, in a manner that instantly lets a user know that an autocorrection has occurred and/or what type of autocorrection is being applied.

Abstract: Embodiments of the disclosure provide methods and apparatuses processing audio data. The method can include: acquiring audio data by an audio capturing device, determining feature information of an enclosure in which the audio capturing device is located, and reverberating the feature information into the audio data.

Abstract: Systems and methods of related to a voice-based system used to determine the severity of emotional distress within an audio recording of an individual is provided. In one non-limiting example, a system comprises a computing device that is configured to receive an audio sample that includes an utterance of a user. Feature extraction is performed on the audio sample to extract a plurality of acoustic emotion features using a base model. Emotion level predictions are generated for an emotion type based at least in part on the acoustic emotion features provided to an emotion specific model. An emotion classification for the audio sample is determined based on the emotion level predictions. The emotion classification comprises the emotion type and a level associated with the emotion type.

Abstract: The invention relates to a method for operating a motor vehicle having an operating device, which includes a speech recognition and language determination device. A recognition of a voice input of a user of the motor vehicle, and a check as to whether a language of the voice input corresponds to the first operating language take place in a first operating mode with a first operating language. Depending on a result of the checking process, a confidence value is assigned to the voice input, which describes a probability with which the language of the voice input is the second operating language. Depending on the assigned confidence value, a query signal is generated, which describes a request, understandable in a second operating language, to the user for indicating the operating mode to be set or the operating language to be set. In response to a received operating signal, the operating mode to be set or the operating language to be set are set.

Abstract: Systems and methods for identifying potential bullying are disclosed. In various aspects, a system for identifying potential bullying includes a sound detector configured to provide samples of sounds over time, a processor, and a memory storing instructions. The instructions, when executed by the processor, cause the system to determine that a noise event has occurred by processing the samples to determine that the sounds exceed a sound level threshold over a time period that exceeds a time period threshold, process the samples to provide frequency spectrum information of the noise event, determine whether the noise event is a potential bullying occurrence based on comparing the frequency spectrum information of the noise event and at least one frequency spectrum profile, and initiate a bullying notification in a case of determining that the noise event is a potential bullying occurrence.

Abstract: A method for automatically translating raw data into real human voiced audio content is provided according to an embodiment of the present disclosure. The method may comprise ingesting data, separating the data into or associating the data with a data type, and creating a list of descriptive data associated with the data type. In some embodiments, the method further comprises compiling audio phrases types associated with the descriptive data, associating a pre-recorded audio file with each audio phrase, and merging a plurality of pre-recorded audio files to create a final audio file.

Abstract: The present disclosure provides a speech synthesis method as well as an apparatus and a computer readable storage medium using the same. The method includes: obtaining a to-be-synthesized text, and extracting to-be-processed Mel spectrum features of the to-be-synthesized text through a preset speech feature extraction algorithm; inputting the to-be-processed Mel spectrum features into a preset ResUnet network model to obtain first intermediate features; performing an average pooling and a first down sampling on the to-be-processed Mel spectrum features to obtain second intermediate features; taking the second intermediate features and the first intermediate features output by the ResUnet network model as an input to perform a deconvolution and a first up sampling so as to obtain target Mel spectrum features corresponding to the to-be-processed Mel spectrum features; and converting the target Mel spectrum features into a target speech corresponding to the to-be-synthesized text.

Abstract: A keyboard instrument includes at least one processor that determines a first pattern of intonation to be applied to a first time segment of a voice data on the basis of a first user operation on a first operation element, causes a first singing voice for the first time segment to be digitally synthesized from the first segment data in accordance with the determined first pattern of intonation, determines a second pattern of intonation to be applied to the second time segment of the voice data on the basis of a second user operation on a second operation element, and causes a second singing voice for the second time segment to be digitally synthesized from the second segment data in accordance with the determined second pattern of intonation.

Abstract: In one aspect, a computerized method for implementing voice and acupressure-based lifestyle management includes the step of measuring a speed at which a user is speaking. A wearable device records the user's voice with a microphone and communicates a digital recording of the user's voice to a computer processor. The method includes the step of measuring a time spacing between a set of user's words and a length of the set of user's words. The method includes the step of determining at least one anomaly by comparing the digital recording of the user's voice with a benchmark recording of the user's voice. The method includes the step of alerting the user of the detected anomaly.

Abstract: Disclosed herein is a speech synthesizer using artificial intelligence including a memory, a communication processor configured to receive utterance information of words uttered by a user from a terminal, and a processor configured to acquire a plurality of utterance intonation phrase (IP) ratios respectively corresponding to a plurality of words uttered by the user based on the utterance information, compare a plurality of IP ratio tables respectively corresponding to a plurality of voice actors with the plurality of utterance IP ratios, acquire a plurality of non-utterance IP ratios respectively corresponding to a plurality of unuttered words based on a result of comparison, and generate a personalized synthesized speech model based on the plurality of utterance IP ratios and the plurality of non-utterance IP ratios.

Abstract: A speech synthesizer using artificial intelligence includes a memory, a communication unit for receiving utterance information of words uttered by a user, and a processor for acquiring a plurality of utterance intonation phrase (IP) ratios respectively corresponding to a plurality of words uttered by the user based on the utterance information, acquiring a plurality of non-utterance IP ratios respectively corresponding to a plurality of unuttered words based on the utterance information and the plurality of utterance IP ratios, and generating a personalized synthesized speech model based on the plurality of utterance IP ratios and the plurality of non-utterance IP ratios. A plurality of classes indicating reading break of a word includes first to third classes. A minor class has a smallest count among the first to third classes. Each of the utterance and non-utterance IP ratios is a ratio in which a word is classified as the minor class.

Abstract: A multi-channel signal encoding method includes determining a downmixed signal of a first channel signal and a second channel signal, an initial reverberation gain parameter of the first channel signal and the second channel signal, determining a target reverberation gain parameter of the first channel signal and the second channel signal based on a correlation between the first channel signal and the downmixed signal, a correlation between the second channel signal and the downmixed signal, and the initial reverberation gain parameter, and quantizing the first channel signal and the second channel signal based on the downmixed signal and the target reverberation gain parameter, and writing a quantized first channel signal and a quantized second channel signal into a bitstream.

Abstract: The present application relates to a system language switching method, a computer readable storage medium, a terminal device, and a device. The method includes first obtaining a preset image for setting a system language of a target terminal, then extracting text information in the image and determining a target language corresponding to the text information, and finally switching the system language of the target terminal to the target language. Through the present application, the user only needs to prepare an image for setting the system language of the target terminal in advance, for example, a piece of paper with Chinese written, and a system can obtain the text information on the image through the processes of image acquisition, text information extraction, and the like, determine that the text message is Chinese, and finally switch the system language of the target terminal to Chinese.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a transfer option for transferring a call. One of the methods include receiving, by a call assistant engine, a keyword related to information provided by a user to an agent during a call; generating, by the call assistant engine, follow-up questions to be displayed on a user device of the agent in an interactive format, the first follow-up question being generated based on the keyword, each of the following follow-up questions being generated based on an answer of the agent to the previous question; and determining, by the call assistant engine, based on answers of the agent to the follow-up questions, a transfer option for transferring the call.