Nokia Corporation

Nokia Mobile Phones Limited

Imre Kiss

Jilei Tian

Akseli Anttila

Harri Wikberg

Paul J Lafata

Olli Viikki

Marcel Cezar Vasilache

Ramalingam Hariharan

Juha H&auml;kkinen

Hemmo Haverinen

Kirsi Karimaki

Input of dictated information in an information processing apparatus is controlled. Utterances of speech are detected and interpreted as words. Word by word confirmation of the interpreted words is detected, the confirmation being associated with an adjustable timeout period. The timeout period may be adjusted according to a number of different measures, including an average time needed for confirmation, an average success rate of dictation, by the pace of dictation as performed by a user, and by a user's history based on statistics from previously performed dictation procedures.

Speech recognition with adjustable timeout period

A mobile communication apparatus capable of presenting themes, a telecommunication system comprising a such apparatus, and a corresponding method are disclosed. The apparatus comprises a processor arranged to generate an audio signal in response to a set theme. The audio signal comprises a speech signal, wherein speech of the speech signal have voice characteristics which depend on the theme. Alternatively, processor is arranged to set a theme in response to contact information stored in a contact information database of the appatatus and associated with actions performed by the apparatus.

Mobile communication terminal

Pattern recognition, wherein a sequence of feature vectors is formed from a digitized incoming signal, the feature vectors comprising feature vector components, and at least one feature vector is compared with templates of candidate patterns by computing a distortion measure. A control signal based on at least one time-dependent variable of the recognition process is formulated, and the distortion measure is computed using only a subset of the vector components of the feature vector, the subset being chosen in accordance with said control signal. This reduces the computational complexity of the computation, as the dimensionality of the vectors involved in the computation is effectively reduced. Although such a dimension reduction decreases the computational need, it has been found not to significantly impair the classification performance.

Pattern recognition

A method for improving noise robustness in speech recognition, wherein a front-end is used for extracting speech feature from an input speech and for providing a plurality of scaled spectral coefficients. The histogram of the scaled spectral coefficients is normalized to the histogram of a training set using Gaussian approximations. The normalized spectral coefficients are then converted into a set of cepstrum coefficients by a decorrelation module and further subjected to ceptral domain feature-vector normalization.

On-line parametric histogram normalization for noise robust speech recognition

A speech recognition feature extractor for extracting speech features from a speech signal, comprising: a time-to-frequency domain transformer (FFT) for generating spectral magnitude values in the frequency domain from the speech signal; a frequency domain filtering block (Mel) for generating a sub-band value relating to spectral magnitude values of a certain frequency sub-band; a compression block (LOG) for compressing said sub-band values; a transformation block (DCT) for obtaining a set of de-correlated features from the compressed sub-band values; and normalising block (CN) for normalising de-correlated features.

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Tampere, Finland

Imre Kiss