Abstract: A user identification method is described in which, in a first identification procedure, identification data (ID1) of a first type belonging to a target individual to be identified are determined and are compared with previously stored user identification data (ND1) of the first type assigned to an authorized user. In addition, identification data (ID2) 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 (ID1) of the first type, user identification data (ND2) of the second type are stored for the respective authorized user using the determined identification data (ID2) of the second type in order to use said data in a subsequent identification procedure. In addition, a corresponding user identification device is disclosed.

Abstract: 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.

Abstract: 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: performance of a speech input; processing of the speech input by means of the speech recognition device for the production of a speech recognition result; if the speech recognition result can be allocated to a function of the electrical appliance, action upon the allocatable function of the electrical appliance; training or adaptation of the speech recognition device on the basis of the speech recognition result associated with the speech input made, if the action upon the allocatable function of the electrical appliance does not cause a user input expressing rejection.

Abstract: A description is given of methods of operating a speech recognition system (1, 10) in which methods a speech signal from a user is analyzed for recognizing speech information contained in the speech signal. In a first method the speech recognition system (1), when situated in an active receive mode, produces an acknowledgement of receive activity (B) in response to an enquiry about receive activity (A) from a user. In a second method, before speech data (S) comprising 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 (I) into an external area (E), the respective speech data (S) are filtered and/or a message is sent to the user that a transmission of speech data (S) to the external area (E) is imminent. In addition, associated speech recognition systems (1, 10) are described.

Abstract: A method is disclosed for the voice control of an appliance in which a voice signal (S) of a user is supplied to a voice recognition device for recognizing a command or a command sequence. Depending on the command recognized by the voice recognition device or the command sequence, an appropriate action (A) or action sequence (AS, AR) of the appliance is performed. A reference time instant (tr) is determined as a function of the occurrence and/or time variation of the voice signal (S). The action (A) or action sequence (AS, AR) of the appliance then takes place in a certain time instant referred to the reference time instant (tr) and/or an action parameter value is determined as a function of the reference time instant (tr), which action parameter value is used in the action (A) or action sequence (AS, AR). In addition, a suitable control system is disclosed.

Abstract: A user identification method is described in which, in a first identification procedure, identification data (ID1) of a first type belonging to a target individual to be identified are determined and are compared with previously stored user identification data (ND1) of the first type assigned to an authorized user. In addition, identification data (ID2) 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 (ID1) of the first type, user identification data (ND2) of the second type are stored for the respective authorized user using the determined identification data (ID2) of the second type in order to use said data in a subsequent identification procedure. In addition, a corresponding user identification device is disclosed.

Abstract: 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 132, 134. Input means 110 are used for receiving a time-sequential input pattern representative of a spoken or written word sequence. A pattern recognizer 120 comprises a word-level matching unit 130 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 132, 134. A cache 150 is used for storing a plurality of most recently recognized words. A sequence-level matching unit 140 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≧2. The probability depends on a frequency of occurrence of the sequence in the cache.

Abstract: 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.

Abstract: 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.

Abstract: Errors occur in some of the recognized words in dictation systems in which the individual words of a text are recognized from a spoken text and displayed, which errors are to be corrected by an operator on the basis of the displayed text. To ascertain more quickly which words are most likely in need of correction, it is suggested according to the invention to determine reliability values for the words, and to display the words in a manner which is dependent on these reliability values. This display may involve, for example, different grey tones, different colors, different letter types, or underlining. It is practical to compare the reliability values with threshold values and to display in a different manner from the remaining text only those words whose reliability values lie below the threshold value or below certain threshold values.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.