Microsoft Technology Licensing, LLC

Sayan Dev Pathak

Christopher Hakan Basoglu

Amit Kumar Agarwal

Hosam Adel Khalil

Steven J White

Duncan Paul Robertson

Khan Mohammad Siddiqui

Yifan Gong

Jamie Daniel Joseph Shotton

Antonio Criminisi

Emilian Y Stoimenov

Chaojun Liu

Piyush Behre

Naveen Parihar

Sharman W Tan

Amy Parag Shah

Shuangyu Chang

Bryan Dove

Ajoy Nandi

Chun Hin Nelson Siu

Zoltan Romocsa

Carmen Quan

Salman Quazi

Ender Konukoglu

Prabhu KrishnaMoorthy

Denis Fisenko

Min Hu

Md Nizam Uddin Chy

Ayush Vikram

Girish Milind Mahajan

Harini Kesavamoorthy

Osama Abuelsorour

Valeriy Viktorovich Kirshin

Systems generate segments of spoken language utterances based on different sets of segmentation boundaries. The systems are also configured to generate one or more formatted segments by assigning a punctuation tags at segmentation boundaries and to generate one or more final sentences from the one or more segments.

Streaming punctuation for long-form dictation

Some disclosed embodiments are directed to obtaining a decoded audio data including a spoken language utterance recognized in audio data and identifying a disfluency in the decoded audio data. Upon determining that correcting the disfluency would improve a readability score of the decoded audio data, the system generates a particular correction to correct the disfluency and applies the particular correction to the decoded audio data. Then, an updated decoded audio data is generated which reflects the particular correction. The updated decoded audio data has improved readability over the decoded audio data.

Systems and methods for GPT guided neural punctuation for conversational speech

A technique partitions a user's original query into plural smaller component queries, each of which has a common part and an instance-specific part. The technique distributes the component queries to plural processor instances of a processor. The plural processor instances transform the respective component queries into query-component responses by acting in parallel, independent of each other. The technique generates a final response based on the query-component responses, e.g., by assembling the component-query responses into the final response. The technique reduces latency because the processor instances work on parts of the user's original query at the same time, rather than as a single stream of consecutive tokens. The plural processor instances have access to a shared cache memory, and utilize relevant data that has been computed in response to previous queries.

Reducing latency by processing parts of a language model query in parallel

Techniques are provided for early processing of a part of a user input to produce a response to the entire or final user input. While the user input is being received, a partial user input, which is a part of the final user input, is processed to produce a response. The response is a candidate response for the final user input. After the final user input is received, and if the partial user input is determined to match or be equivalent to the final user input, the first response, which is already available, is provided to one or more output devices for presentation. If the final user input is determined to differ from the partial user input, the final user input is processed to produce a second response to the final user input, and the second response is provided for presentation. In some instances, multiple partial user inputs are received and processed.

Processing part of a user input to produce an early response

A data processing system implements obtaining a first textual content, segmenting the first textual content into a plurality of first segments, and providing each segment of the plurality of first segments to a first natural language processing (NLP) model to obtain a set of first readability scores for the plurality of first segments. The first NLP model is configured to analyze a textual input and to output a readability score representing a measurement of readability of the textual input. The system further implements aggregating the set of first segment readability scores to determine a first readability score for the first textual content, and perform at least one of causing the first readability score to be presented to a user or performing one or more actions on the first textual content based on the readability score.

Automated artificial intelligence driven readability scoring techniques

Acoustic features are batched into two different batches. The second batch of the two batches is made in response to a detection of a word hypothesis output by a speech recognition network that received the first batch. The number of acoustic feature frames of the second batch is equal to a second batch size greater than the first batch size. The second batch is also to the speech recognition network for processing.

Adaptive batching to reduce recognition latency

Embodiments may include collection of a first batch of acoustic feature frames of an audio signal, the number of acoustic feature frames of the first batch equal to a first batch size, input of the first batch to a speech recognition network, collection, in response to detection of a word hypothesis output by the speech recognition network, of a second batch of acoustic feature frames of the audio signal, the number of acoustic feature frames of the second batch equal to a second batch size greater than the first batch size, and input of the second batch to the speech recognition network.

Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.

Image registration

An example for processing patient image data workflows can temporarily serially arrange a set of semantic labeling modules in a patient image data workflow pipeline responsive to receiving an event trigger. The example can also remove the set of modules from the patient image data workflow pipeline responsive to receiving an event completion trigger.

Metadata extraction pipeline

Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.

Automatic organ localization

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Kirkland, WA, United States of America

Sayan Dev Pathak