Nuance Communications, Inc.

International Business Machines Corporation

Nathan M Bodenstab

Paul Joseph Vozila

Bhuvana Ramabhadran

Gakuto Kurata

William F Ganong, Iii

Masafumi Nishimura

Nobuyasu Itoh

Vladimir Sejnoha

Ron Hoory

Abhinav Sethy

Gunnar Evermann

Jonathan Mamou

Marc W Regan

Stephen W Laverty

Gary B Clayton

Yik-Cheung Tam

Dermot Connolly

Vittorio Manzone

Stefan Hahn

Paul Maergner

Kenneth Smith

Matt Hohensee

Victor S Chen

A method, computer program product, and computer system for identifying, by a computing device, at least one language model component of a plurality of language model components in at least one application associated with automatic speech recognition (ASR) and natural language understanding (NLU) usage. A contribution bias may be received for the at least one language model component. The ASR and NLU may be aligned between the plurality of language model components based upon, at least in part, the contribution bias.

System and method for aligning ASR model weights with NLU concepts

Methods and systems are provided for improving speech recognition of multilingual named entities. In some embodiments, a list comprising a plurality of named entities may be accessed by a computing device. A first named entity represented in the native language may be compared with the first named entity represented in the foreign language. One or more words that appear in both the first named entity represented in the native language and the first named entity represented in the foreign language may be identified as one or more foreign words. A grapheme-to-phoneme (G2P) conversion may be applied to the one or more foreign words, wherein graphemes of the one or more foreign words are mapped to phonemes in the native language. The G2P conversion may result in a native pronunciation for each of the one or more foreign words, which are added to a recognition dictionary along with the native pronunciations.

Improving automatic speech recognition of multilingual named entities

Techniques disclosed herein include systems and methods for voice-enabled searching. Techniques include a co-occurrence based approach to improve accuracy of the 1-best hypothesis for non-phrase voice queries, as well as for phrased voice queries. A co-occurrence model is used in addition to a statistical natural language model and acoustic model to recognize spoken queries, such as spoken queries for searching a search engine. Given an utterance and an associated list of automated speech recognition n-best hypotheses, the system rescores the different hypotheses using co-occurrence information. For each hypothesis, the system estimates a frequency of co-occurrence within web documents. Combined scores from a speech recognizer and a co-occurrence engine can be combined to select a best hypothesis with a lower word error rate.

Accuracy improvement of spoken queries transcription using co-occurrence information

Methods and a system for calculating N-gram probabilities in a language model. A method includes counting N-grams in each page of a plurality of pages or in each document of a plurality of documents to obtain respective N-gram counts therefor. The method further includes applying weights to the respective N-gram counts based on at least one of view counts and rankings to obtain weighted respective N-gram counts. The view counts and the rankings are determined with respect to the plurality of pages or the plurality of documents. The method also includes merging the weighted respective N-gram counts to obtain merged weighted respective N-gram counts for the plurality of pages or the plurality of documents. The method additionally includes calculating a respective probability for each of the N-grams based on the merged weighted respective N-gram counts.

Correcting N-gram probabilities by page view information

Some embodiments relate to a method of performing a search for content on the Internet, in which a user may speak a search query and speech recognition may be performed on the spoken query to generate a text search query to be provided to a plurality of search engines. This enables a user to speak the search query rather than having to type it, and also allows the user to provide the search query only once, rather than having to provide it separately to multiple different search engines.

Method and apparatus for processing spoken search queries

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Melrose, MA, United States of America

Nathan M Bodenstab