Nokia Corporation

Juha Iso-Sipila

Leon Hurst

Bogdan Barliga

Mikko A Harju

An apparatus for providing flexible text based language identification includes an alphabet scoring element, an n-gram frequency element and a processing element. The alphabet scoring element may be configured to receive an entry in a computer readable text format and to calculate an alphabet score of the entry for each of a plurality of languages. The n-gram frequency element may be configured to calculate an n-gram frequency score of the entry for each of the plurality of languages. The processing element may be in communication with the n-gram frequency element and the alphabet scoring element. The processing element may also be configured to determine a language associated with the entry based on a combination of the alphabet score and the n-gram frequency score.

Method, apparatus and computer program product for providing flexible text based language identification

A method for organizing data records in a memory in a mobile telecommunication terminal is disclosed. The method comprises receiving a plurality of digits, which identify a subscriber terminal in a telecommunication network, in a control unit in the mobile, telecommunication terminal. The method moreover comprises storing the plurality of digits in a memory in the mobile telecommunication terminal, and associating the plurality of digits with dictionary selection data designating at least one dictionary stored in the memory.

Mobile communication terminal and method therefore

A method and apparatus for speech processing in a distributed speech recognition system having a front-end and a back-end. The speech processing steps in the front-end are as follows: extracting speech features from a speech signal and normalizing the speech features in order to alter the power of the noise component in the modulation spectrum in relation to the power of the signal component, especially with frequencies above 10 Hz. A low-pass filter is then used to filter the normalized modulation spectrum in order to improve the signal-to-noise ratio (SNR) in the speech signal. The combination of feature vector normalization and low-pass filtering is effective in noise removal, especially in a low SNR environment.

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Tampere, Finland

Juha Iso-Sipila