International Business Machines Corporation

Amazon Technologies, Inc.

Nuance Communications, Inc.

Siegfried Kunzmann

Volker Fischer

Athanasios Mouchtaris

Maurizio Omologo

Feng-Ju Chang

Ossama S Emam

Brian John King

Burn Lewin Lewis

Werner Kriechbaum

Upali Bandara

Carsten Guenther

Karlheinz Mohr

Martin Radfar

Claire Waast-Ricard

Jing Liu

A Jon Tyrrell

Bernhard Hubert Zeller

Eric-W Janke

Techniques for performing automatic speech recognition (ASR) are described. In some embodiments, an ASR component integrates contextual information from user profile data into audio encoding data to predict a token(s) corresponding to a spoken input. The user profile data may include personalized words, such as, contact names, device names, etc. The ASR component determines word embedding data using the personalized words. The ASR component is configured to apply attention to audio frames that are relevant to the personalized words based on processing the audio encoding data and the word embedding data.

Automatic speech recognition

A multi-channel transformer acoustic model that processes a plurality of audio signals output by microphones of a microphone array and outputs probabilities for acoustic units of an utterance represented in the audio signals. The audio signals represent the individual microphones' respective capturing of the utterance. The multi-channel model may perform self-attention on embeddings of the audio signals and then cross-channel attention across the attended audio signals. The cross-channel attention may involve processing of signals relative to each other to model the relationships across channels within and across time frames. The multi-channel model may include a transducer to perform processing frame-by-frame.

The present invention relates to computer-generated text-to-speech conversion. It relates in particular to a method and system for updating a Concatenative Text-To-Speech (CTTS) system with a speech database from a base version to a new version. The present invention performs an application-specific re-organization of a synthesizer's speech database by means of certain decision tree modifications. By that reorganization, certain synthesis units are made available for the new application, which are not available in prior art without a new speech session. This allows the creation of application-specific synthesizers with improved output speech quality for arbitrary domains and applications at very low cost.

Back-end database reorganization for application-specific concatenative text-to-speech systems

A method and respective system for operating a speech recognition system, in which a plurality of recognizer programs are accessible to be activated for speech recognition, and are combined on a per need basis in order to efficiently improve the results of speech recognition done by a single recognizer. In order to adapt such system to the dynamically changing acoustic conditions of various operating environments and to the particular requirements of running in embedded systems having only a limited computing power available, it is proposed to a) collect selection base data characterizing speech recognition boundary conditions, e.g. the speaker person and the environmental noise, etc., with sensor means, and b) using program-controlled arbiter means for evaluating the collected data, e.g., a decision engine including software mechanism and a physical sensor, to select the best suited recognizer or a combination thereof out of the plurality of available recognizers.

Sensor based approach recognizer selection, adaptation and combination

The present invention relates to a computer system and to a method for encoding of information into a representation comprising a plurality of segments, the order of the segments in the representation being irrelevant for a rendering of the representation, the method comprising the steps of:

Method and computer system for encoding of information into a representation

The present invention provides a computerized method and apparatus for automatically generating from a first speech recognizer a second speech recognizer which can be adapted to a specific domain. The first speech recognizer can include a first acoustic model with a first decision network and corresponding first phonetic contexts. The first acoustic model can be used as a starting point for the adaptation process. A second acoustic model with a second decision network and corresponding second phonetic contexts for the second speech recognizer can be generated by re-estimating the first decision network and the corresponding first phonetic contexts based on domain-specific training data.

Method and apparatus for phonetic context adaptation for improved speech recognition

Computer-based methods and systems are provided for automatically generating, from a first speech recognizer, a second speech recognizer such that the second speech recognizer is tailored to a certain application and requires reduced resources compared to the first speech recognizer. The invention exploits the first speech recognizer's set of states s and set of probability density functions (pdfs) assembling output probabilities for an observation of a speech frame in said states s . The invention teaches a first step of generating a set of states of the second speech recognizer reduced to a subset of states of the first speech recognizer being distinctive of the certain application. The invention teaches a second step of generating a set of probability density functions of the second speech recognizer reduced to a subset of probability density functions of the first speech recognizer being distinctive of the certain application.

Method and system for generating squeezed acoustic models for specialized speech recognizer

A speech recognition system and a method executed by a speech recognition system focusing on the vocabulary of the speech recognition system and its usage during the speech recognition process is provided. A segmented vocabulary and its exploitation is provided comprising a multitude of entries wherein an entry is either identical to a legal word or a constituent of a legal word of the language, and the constituent is an arbitrary sub-component of the legal word according to the orthography. A constituent can comprise any number of characters not limited to a syllable of a legal word or a recognition unit of the speech recognition system. The vocabulary is used to recognize constituents of the vocabulary for recombination of the constituents into legal words if a constituent combination table indicates that the recognized constituents are a legal concatenation in the language.

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Heidelberg, Germany

Siegfried Kunzmann