Microsoft Technology Licensing, LLC

Microsoft Corporation

Zhenghao Wang

Xuedong D Huang

Jianfeng Gao

Huaming Wang

Jinyu Li

Yifan Gong

Bradley Gene Calder

William B Dolan

Andrew James Edwards

Christopher Brian Quirk

Christopher John Brockett

Mei-Yuh Hwang

Zhuo Chen

Takuya Yoshioka

Ravi Chandru Shahani

Xiong Xiao

Satish Narayanasamy

Qing Chang

James Jordan Tigani

Mei Li

Roman Holenstein

Representative embodiments disclose mechanisms to separate and recognize multiple audio sources (e.g., picking out individual speakers) in an environment where they overlap and interfere with each other. The architecture uses a microphone array to spatially separate out the audio signals. The spatially filtered signals are then input into a plurality of separators, so each signal is input into a corresponding signal. The separators use neural networks to separate out audio sources. The separators typically produce multiple output signals for the single input signals. A post selection processor then assesses the separator outputs to pick the signals with the highest quality output. These signals can be used in a variety of systems such as speech recognition, meeting transcription and enhancement, hearing aids, music information retrieval, speech enhancement and so forth.

Multi-channel speech separation

Statistical Machine Translation (SMT) based search query spelling correction techniques are described herein. In one or more implementations, search data regarding searches performed by clients may be logged. The logged data includes query correction pairs that may be used to ascertain error patterns indicating how misspelled substrings may be translated to corrected substrings. The error patterns may be used to determine suggestions for an input query and to develop query correction models used to translate the input query to a corrected query. In one or more implementations, probabilistic features from multiple query correction models are combined to score different correction candidates. One or more top scoring correction candidates may then be exposed as suggestions for selection by a user and/or provided to a search engine to conduct a corresponding search using the corrected query version(s).

Statistical machine translation based search query spelling correction

Techniques and architectures may be used to generate and perform a process using weighted finite-state transducers involving generic input search graphs. The process need not pursue theoretical optimality and instead search graphs may be optimized without an a priori optimization step. The process may result in an automatic speech recognition (ASR) decoder that is substantially faster than ASR decoders the include the optimization step.

No loss-optimization for weighted transducer

The subject disclosure is directed towards developing a translation model for mapping search query terms to document-related data. By processing user logs comprising search histories into word-aligned query-document pairs, the translation model may be trained using data, such as probabilities, corresponding to the word-aligned query-document pairs. After incorporating the translation model into model data for a search engine, the translation model is used may used as features for producing relevance scores for current search queries and ranking documents/advertisements according to relevance.

Search query and document-related data translation

Computer-readable media, computer systems, and computing methods are provided for recommending websites that are relevant to a current website to which a user has navigated. A search engine is used to track a set of websites the user has visited immediately prior to the current website, while predictive model(s) are used to generate a sequence of websites that include the current website and the tracked websites. The sequence is compared against strings of websites within a browser-history log to identify matching strings, where the matching strings include the sequence and a respective candidate website. A probability of relevance is computed from a frequency that each of the matching strings has been visited within a predefined time frame. The probability of relevance for each of the matching strings is ranked against one another to distill the highest-ranked matching strings, which are parsed to extract and present the candidate websites included therein.

Predictively suggesting websites

An apparatus, method and computer-readable medium provide for detecting and filtering potential race conditions. In one example, potential race conditions may be detected by determining overlap of memory accesses (e.g., read, write). Potential race conditions may be filtered as potentially benign or harmful race conditions using, for example, heuristics, comparison of output states from execution of instructions or operations in differing sequences or identifying of associated output bugs corresponding to the execution of operations in different sequences.

Machine instruction level race condition detection

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Redmond, WA, United States of America

Zhenghao Wang