Sony Deutschland Gmbh

Sony International (Europe) Gmbh

Other

Sony Corporation

Ralf Kompe

Thomas Kemp

Silke Goronzy

Raquel Tato

Yin Hay Lam

Krzysztof Marasek

Peter Buchner

Zica Valsan

Sunna Torge

Martin Emele

Stefan Rapp

Helmut Wais

Robert Mencl

Christian Hying

Kenta Kawamoto

Masahiro Fujita

Katsuki Minamino

Naoto Iwahashi

Helmut Lucke

Rika Horinaka

Daniela Raddino

Jason Williams

Franck Giron

Silke Goronzy-Thomae

The present invention discloses an apparatus for automatic extraction of important events in audio signals comprising:

Apparatus and method for automatic extraction of important events in audio signals

The invention provides a method for processing speech comprising the steps of receiving a speech input (SI) of a speaker, generating speech parameters (SP) from said speech input (SI), determining parameters describing an absolute loudness (L) of said speech input (SI), and evaluating (EV) said speech input (SI) and/or said speech parameters (SP) using said parameters describing the absolute loudness (L). In particular, the step of evaluation (EV) comprises a step of emotion recognition and/or speaker identification. Further, a microphone array comprising a plurality of microphones is used for determining said parameters describing the absolute loudness. With a microphone array the distance of the speaker from the microphone array can be determined and the loudness can be normalized by the distance. Thus, the absolute loudness becomes independent from the distance of the speaker to the microphone, and absolute loudness can now be used as an input parameter for emotion recognition and/or speaker identification.

Method for processing speech using absolute loudness

A user profile and/or the suggestions computed based thereon are obtained taking a special set of user features into account. The user features are defined to represent a typical general behaviour of an individual user in respect to the application where the user profile is used. In other words, for each application where a user profile is used a special set of user features are defined which are able to represent a typical general behaviour of an individual user. Based on these user features the weights in the list of word-weight pairs or weighted keywords which represents the user profile are computed or influenced during the creation of the user profile, and/or a mufti-user profile is split during the creation of an individual user profile from a mufti-user profile, and/or during specification of a suggestion a user history which is used to create the user profile, and/or the user profile, and/or the suggestion results are filtered.

Methods to create a user profile and to specify a suggestion for a next selection of a user

An apparatus for automatic dissection of segmented audio signals, wherein at least one information signal for identifying programs included in said audio signals and for identifying contents included in said programs. Content detection device detects programs and contents belonging to the respective programs in the information signal. Program weighting device weights each program includes in the information signal based on the contents of the respective program detected by the content detection device. Program ranking device indentifies programmers of the same category and ranking said programs based on a weighting result for each program provided by the program weighting device.

Apparatus and method for automatic dissection of segmented audio signals

An apparatus for entering data into a computing device includes a graphical user interface that presents hierarchically organized information in a menu structure of at least two hierarchy levels, including a topmost hierarchy level and at least one further hierarchy level. The apparatus also includes at least two haptic keys, each having more than one state of activation. Each of the haptic keys is assigned to a particular hierarchy level. A first haptic key is assigned to the topmost hierarchy level. A menu on the topmost hierarchy level is directly accessible using the first haptic key. A menu on a hierarchy level higher than one that is currently presented on the graphical user interface is directly accessible using a haptic key assigned to the menu on the higher level, when a hierarchy level of the currently presented menu is one of the at least one further hierarchy level.

Haptic key controlled data input

The method of operating a man-machine interface unit includes classifying at least one utterance of a speaker to be of a first type or of a second type. If the utterance is classified to be of the first type, the utterance belongs to a known speaker of a speaker data base, and if the utterance is classified to be of the second type, the utterance belongs to an unknown speaker that is not included in the speaker data base. The method also includes storing a set of utterances of the second type, clustering the set of utterances into clusters, wherein each cluster comprises utterances having similar features, and automatically adding a new speaker to the speaker data base based on utterances of one of the clusters.

Spoken man-machine interface with speaker identification

To increase the robustness and/or the recognition rate of methods for recognizing speech it is proposed to include phone boundary verification measure features in the process of obtaining and/or generating confidence measures obtained recognition results.

Method for recognizing speech

An audio data segmentation apparatus for segmenting of audio data including for supplying audio data, dividing the audio data supplied into audio clips of a predetermined length, discriminating the audio clips into predetermined audio classes, the audio classes identifying a kind of audio data included in the respective audio clip and segmenting for segmenting the audio data into audio meta patterns based on a sequence of audio classes of consecutive audio clips, each meta pattern being allocated to a predetermined type of contents of the audio data. It is difficult to achieve good results with known methods for segmentation of audio data into meta patterns since the rules for the allocation of the meta patterns are dissatisfying. This problem is solved by the inventive audio data segmentation apparatus further including a program database including program data units to identify a certain kind of program, a plurality of respective audio meta patterns being allocated to each program data unit, wherein the segmenting segments the audio data into corresponding audio meta patterns on the basis of the program data units of the program database

Apparatus and method for segmentation of audio data into meta patterns

The present invention provides a method for operating and/or for controlling a man-machine interface unit (MMI) for a finite user group environment. Utterances out of a group of user are repeatedly received. A process of user identification is carried out based on said received utterances. The process of user identification comprises a set of clustering so as to enable an enrolment-free performance.

A user profile and/or the suggestions computed based thereon are obtained taking a special set of user features into account. The user features are defined to represent a typical general behaviour of an individual user in respect to the application where the user profile is used. In other words, for each application where a user profile is used a special set of user features are defined which are able to represent a typical general behaviour of an individual user. Based on these user features the weights in the list of word-weight pairs or weighted keywords which represents the user profile are computed or influenced during the creation of the user profile, and/or a multi-user profile is split during the creation of an individual user profile from a multi-user profile, and/or during specification of a suggestion a user history which is used to create the user profile, and/or the user profile, and/or the suggestion results are filtered.

To reduce the error rate when classifying emotions from an acoustical speech input (SI) only, it is suggested to include a process of speaker identification to obtain certain speaker identification data (SID) on the basis of which the process of recognizing an emotional state is adapted and/or configured. In particular, speaker-specific feature extractors (FE) and/or emotion classifiers (EC) are selected based on said speaker identification data (SID).

Method for detecting emotions from speech using speaker identification

To increase the recognition rate and quality in a process of recognizing speech an approximative set of pronunciation rules (APR) for a current pronunciation (CP) of a current speaker is determined in a given pronunciation space (PS) and then applied to a current pronunciation lexicon (CL) so as to perform a speaker specific adaptation of said current lexicon (CL).

Method for recognizing speech using eigenpronunciations

A method for recognizing speech is proposed wherein the process of recognition is started using the starting acoustic model (SAM) and wherein the current acoustic model (CAM) is modified by removing or cancelling model function mixture components (MFM) which are negligible for the description of the speaking behavior and quality of the current speaker. Therefore, the size of the acoustic model (SAM, CAM) is reduced by adaptation to the current speaker enabling fast performance and increased recognition efficiency.

Recognizing speech by selectively canceling model function mixture components

To avoid an over-adaptation of a current acoustic model (CAM) to certain and frequently occuring words for speech phrases during on-line speaker adaptation of speech recognizers it is suggested to count adaptation numbers (a) for each of said speech phrases (SPj) as numbers of times in that a distinct speech phrase (SPj) has been used as a basis for adapting said current acoustic model (CAM) and further to make the strength of adaptation of the current acoustic model (CAM) on the basis of said distinct speech phrase (SPj) dependent on its specific adaptation number (a) so as to decrease the influence of frequent speech phrases (SPj) in the received speech flow on the adaptation process.

Method for recognizing speech to avoid over-adaptation during online speaker adaptation

There is proposed a method that may be universally used for controlling a man-machine interface unit. A learning sample is used in order at least to derive and/or initialize a target action (t) to be carried out and to lead the user from an optional current status (e) to an optional desired target status (e) as the final status (e). This learning sample (l) is formed by a data triple made up by an initial status (e) before an optional action (a) carried out by the user, a final status (e) after the action taken place (a).

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Fellbach, Germany

Ralf Kompe