Microsoft Technology Licensing, LLC

Cisco Technology, Inc.

Karthik Raghunathan

Sarangarajan Parthasarathy

Umut Ozertem

Michael Levit

Padma Varadharajan

Issac Alphonso

Arushi Raghuvanshi

Vijay Ramakrishnan Thimmaiyah

Lucien Serapio Carroll

Varsha Ravikumar Embar

Benoit F Dumoulin

Rosie Jones

Milad Shokouhi

Nick Craswell

Ali Ahmadi

Systems, methods, and computer-readable media for correcting transcriptions created through automatic speech recognition. A transcription of speech created using an automatic speech recognition system can be received. One or more domain-specific contexts associated with the speech can be identified and a text span that includes a mistranscribed entry can be recognized from the speech based on the one or more domain-specific contexts. Additionally, features can be extracted from the mistranscribed entry and the extracted features can be matched against an index of domain-specific entries to identify a correct entry of the mistranscribed entry. Subsequently, the transcription can be corrected by replacing with the mistranscribed entry with the correct entry.

Automatic speech recognition correction

This disclosure describes techniques for generating a conversation summary. The techniques may include processing at least one statement indication of the conversation to determine at least one statement that is a candidate highlight of the conversation. The techniques may further include applying linguistic filtering rules to the candidate highlight to determine the candidate highlight is an actual highlight. The techniques may further include generating the conversation summary including providing the actual highlight as at least a portion of the conversation summary.

Automatic extraction of conversation highlights

Methods and systems are provided for providing alternative query suggestions. For example, a spoken natural language expression may be received and converted to a textual query by a speech recognition component. The spoken natural language expression may include one or more words, terms, and/or phrases. A phonetically confusable segment of the textual query may be identified by a classifier component. The classifier component may determine at least one alternative query based on identifying at least the phonetically confusable segment of the textual query. The classifier may further determine whether to suggest the at least one alternative query based on whether the at least one alternative query is sensical and/or useful. When it is determined to suggest the at least one alternative query, the at least one alternative query may be provided to and displayed on a user interface display.

Interactive reformulation of speech queries

Examples of the present disclosure describe generation of a multi-arc confusion network to improve, for example, an ability to return alternatives to output generated. A confusion network comprising token representations of lexicalized hypotheses and normalized hypotheses is generated. Each arc of the confusion network represents a token of a lexicalized hypothesis or a normalized hypothesis. The confusion network is transformed into a multi-arc confusion network, wherein the transforming comprising realigning at least one token of the confusion network to span multiple arcs of the confusion network. Other examples are also described.

Transcription correction using multi-token structures

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Sunnyvale, CA, United States of America

Karthik Raghunathan