Koninklijke Philips Corporation N.V.

U.S. Philips Corporation

Nuance Communications, Inc.

Volker Steinbiss

Hermann Ney

Bach-Hiep Tran

Wolfgang Otto Budde

Dietrich Klakow

Frank Seide

Thomas Eisele

A user identification method is described in which, in a first identification procedure, identification data (ID) of a first type belonging to a target individual to be identified are determined and are compared with previously stored user identification data (ND) of the first type assigned to an authorized user. In addition, identification data (ID) of a second type that belong with a certain probability to the same target individual are automatically determined. After a successful confirmation of the identify of the target individual with the authorized user from the identification data (ID) of the first type, user identification data (ND) of the second type are stored for the respective authorized user using the determined identification data (ID) of the second type in order to use said data in a subsequent identification procedure. In addition, a corresponding user identification device is disclosed.

User identification method and device

In methods of operating a speech recognition system, a speech signal from a user is analyzed for recognizing speech information contained in the speech signal. When situated in an active receive mode, an acknowledgement of receive activity is produced in response to an inquiry about the receive activity from a user. In another embodiment, before speech data including at least a portion of the speech signal and/or at least a portion of the speech information are transmitted from an internal user-controlled area into an external area, the respective speech data are filtered and/or a message is sent to the user that a transmission of the speech data to the external area is imminent.

Method of operating a speech recognition system

The invention relates to a method for the training or adaptation of a speech recognition device used to act upon functions of an electrical appliance, for example the triggering of a voice dial in a mobile telephone terminal. In order to structure the training and/or adaptation of the speech recognition device to improve user comfort, a method is proposed with the following steps:

Method for the training or adaptation of a speech recognition device

A small vocabulary pattern recognition system is used for recognizing a sequence of words, such as a sequence of digits (e.g. telephone number) or a sequence of commands. A representation of reference words is stored in a vocabulary . Input means are used for receiving a time-sequential input pattern representative of a spoken or written word sequence. A pattern recognizer comprises a word-level matching unit for generating a plurality of possible sequences of words by statistically comparing the input pattern to the representations of the reference words of the vocabulary . A cache is used for storing a plurality of most recently recognized words. A sequence-level matching unit selects a word sequence from the plurality of sequences of words in dependence on a statistical language model which provides a probability of a sequence of M words, M&gE;2. The probability depends on a frequency of occurrence of the sequence in the cache. In this way for many small vocabulary systems where no reliable data is available on frequency of use of word sequences, the cache is used to provide data representative of the actual use.

Language model based on the speech recognition history

The distance computation represents a central, constantly recurrent task in sample and speech recognition. It is used in speech recognition as a degree of similarity between a part of a speech utterance and a speech reference. In picture processing and sample recognition, it is used for data compression. The distance computation requires the longest computation time so that a reduction of the computation time results in a considerable efficiency improvement. A reduction of the computation time is achieved by the integration of the distance computation in a memory module in which particularly the reference data are stored. Due to this integration, the other components of the overall system are relieved of this constantly recurrent task and are available for more complex processes in this period of time. This integration makes the distance computation essentially shorter because the communication between memory sections and computation unit takes place directly without utilizing a busy system.

Memory having speech recognition support, by integrated local distance-computation/reference-vector-storage, for applications with general-purpose microprocessor systems

The determination of a plurality of sequences of words from a speech signal with a decreasing probability of correspondence utilizes the best word sequence as a basis and as further word sequences there are determined only those which enclose a part of the best word sequence, that is to say the remainder of these word sequences. To this end, the recognition involves first the formation of a word graph and the best word sequence is separately stored as a tree which initially has one branch only. The word boundaries of this word sequence form nodes in this tree. Because only nodes of this tree have to be taken into account for the next-best word sequences, the calculation is substantially simpler than if the complete word graph were first completely expanded in the form of a tree and completely searched again for each new word sequence.

Method of and apparatus for deriving a plurality of sequences of words

When a language model is to be used for the recognition of a speech signal and the vocabulary is composed as a tree, the language model value cannot be taken into account before the word end. Customarily, after each word end the comparison with a tree root is started anew, be it with a score which has been increased by the language model value so that the threshold value for the scores at which hypotheses are terminated must be high and hence many, even unattractive hypotheses remain active for a prolonged period of time. In order to avoid this, in accordance with the invention a correction value is added to the score for at least a part of the nodes of the vocabulary tree; the sum of the correction values on the path to a word then may not be greater than the language model value for the relevant word. As a result, for each test signal the scores of all hypotheses are of a comparable order of magnitude. When a word end is reached, the sum of all added correction values is subtracted and the correct language model value is added.

Method of recognizing a sequence of words and device for carrying out

In the recognition of coherent speech, language models are favourably used to increase the reliability of recognition, which models, for example, take into account the probabilities of word combinations, especially of word pairs. For this purpose, a language model value corresponding to this probability is added at boundaries between words. In several recognition methods, for example, when the vocabulary is built up from phonemes in the shape of a tree, it is not known at the start of the continuation of a hypothesis after a word end which word will actually follow, so that a language model value cannot be taken into account until at the end of the next word. Measures are given for achieving this in such a manner that as far as possible the optimal preceding word or the optimal preceding word sequence is taken into account for the language model value without the necessity of constructing a copy of the searching tree for each and every simultaneously ending preceding word sequence.

Method and apparatus for recognizing spoken words in a speech signal

In a known method of recognizing a word string in a speech signal, a new specific organization of the storage locations in the memory containing the trace-back addresses is proposed. Furthermore, a step for generating the information for these storage locations is proposed which makes it possible to determine not only the word string with the best similarity, but also a specific number of further word strings of decreasing similarity. Thus, the additional computing capacity required for determining these further word strings is small relative to the computing capacity required for the remainder of the recognition process.

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Aachen, Germany

Volker Steinbiss