초록 ▼

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-

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.

대표청구항 ▼

1. A speech recognition feature extractor for extracting speech features from a speech signal, comprising: a time-to-frequency domain transformer for generating spectral magnitude values in the frequency domain from the speech signal; a frequency domain filtering block for generating a sub-band valu

1. A speech recognition feature extractor for extracting speech features from a speech signal, comprising: a time-to-frequency domain transformer for generating spectral magnitude values in the frequency domain from the speech signal; a frequency domain filtering block for generating a sub-band value relating to spectral magnitude values of a certain frequency sub-band, for each of a group of frequency sub-bands; a compression block for compressing said sub-band values; a transformat on block for obtaining a set of de-correlated features from the sub-band values; and a normalising block for normalizing features; said feature extractor comprising a mean emphasising block for emphasizing at least one of the sub-band values after frequency domain filtering, wherein the emphasising is accomplished by addition of a mean value of sub-band signals to said at least one of the sub-band values. 2. A speech recognition feature extractor according to claim 1, wherein the mean emphasising block is arranged to mean emphasise all the sub-band values. 3. A speech recognition feature extractor according to claim 1, wherein the mean emphasising block is arranged to mean emphasise some of the sub-band values. 4. A speech recognition feature extractor according to claim 1, wherein said frequency domain filtering block is arranged to generate sub-band values according to a scale based on an auditory model. 5. A speech recognition feature extractor according to claim 1, comprising a differentiation block for generation of first time derivatives and second time derivatives for each of said de-correlated features; and wherein said normalising block is arranged to generate normalised speech features using said de-correlated features, said first derivatives features, and said second derivatives. 6. A speech recognition feature extractor according to claim 1, wherein, in said addition of the mean value, said mean emphasising block is arranged to add a mean estimate term to each sub-band value that is to be mean emphasised. 7. A speech recognition feature extractor according to claim 6, wherein the mean emphasising block is arranged to calculate the mean estimate term from compressed sub-band values representing a series of at least two subsequent speech frames. 8. A mobile station comprising a speech recognition feature extractor for extracting speech features from a speech signal, said extractor comprising: a time-to-frequency domain transformer for generating spectral magnitude values in the frequency domain from the speech signal; a frequency domain filtering block for generating a sub-band value relating to spectral magnitude values of a certain frequency sub-band, for each of a group of frequency sub-bands; a compression block for compressing said sub-band values; a transformation block for obtaining a set of de-correlated features from the sub-band values; and a normalising block for normalising features; said feature extractor comprising a mean emphasising block for emphasising; at least one of the sub-band values after frequency domain filtering, wherein the emphasising is accomplished by addition of a mean value of sub-band signals to said at least one of the sub-band values. 9. A method for extracting speech features from a speech signal, comprising the steps of: generating spectral magnitude values in the frequency domain from the speech signal; generating a sub-band value relating to spectral magnitude, values of a certain frequency sub-band; compressing said sub-band values; obtaining a set of de-correlated features from the sub-band values; normalising features; and emphasising at least one of the sub-band values after frequency domain filtering, wherein the emphasising is accomplished by addition of a mean value of sub-band signals to said at least one of the sub-band values. 10. A computer program for extracting speech features from a speech signal, comprising: a computer readable program means for causing a computer to g enerate spectral magnitude values in the frequency domain from the speech signal; a computer readable program means for causing a computer to generate a sub-band value relating to spectral magnitude values of a certain frequency sub-band, for each of a group of frequency sub-bands; a computer readable program means for causing a computer to compress said sub-band values; a computer readable program means for causing a computer to obtain a skit of de-correlated features from the sub-band values; a computer readable program means for causing a computer to normalise features; and a computer readable mean-emphasising program means for causing a computer to emphasise at least one of the sub-band values after frequency domain filtering, wherein emphasising is accomplisher by addition of a mean value of sub-band signals to said at least one of the sub-band values.