Speech recognition with language-dependent model vectors
View Patent ↗Speaker-dependent speech recognition is performed upon detecting a speech signal encompassing a voice command. The speech signal is divided into time frames and characterized in each detected time frame by forming a corresponding property vector. A language-independent feature vector sequence is formed from one or several property vectors and then stored. The language-independent feature vector sequence is allocated to a language-dependent sequence of model vectors in a speech resource having a plurality of model vectors. A piece of allocation information indicating allocation of the language-independent feature vector sequence to a language-dependent sequence of model vectors is stored, then the voice command allocated to the model vector sequence is identified.
1. A method for speaker-dependent speech recognition, comprising:
capturing a speech signal, including a speech command, of a speaker;
breaking down the speech signal into time frames;
characterizing the speech signal in each captured time frame by forming a corresponding feature vector;
forming a language-independent feature vector sequence from at least one feature vector;
storing the language-independent feature vector sequence;
assigning the language-independent feature vector sequence to a language-dependent sequence of model vectors in a first language resource which includes a multiplicity of language-dependent model vectors;
storing first assignment information which specifies assignment of the language-independent feature vector sequence to the language-dependent sequence of model vectors;
recognizing the speech command which is assigned to the language-dependent sequence of model vectors;
selecting a second language resource different from the first language resource;
assigning the language-independent feature vector sequence previously stored to a language-dependent model vector sequence in the second language resource; and
storing second assignment information regarding said assigning of the language-independent feature vector sequence to the language-dependent model vector sequence in the second language resource.
2. A method as claimed in claim 1 , wherein the speech signal is made up of acoustic units.
3. A method as claimed in claim 2 , wherein each of the first and second language resources is based on a Hidden Markov Modeling of acoustic units of a speech signal.
4. A method as claimed in claim 3 , wherein an acoustic unit is formed by a word or a phoneme.
5. A method as claimed in claim 3 , wherein an acoustic unit is formed by word segments or groups of related phonemes.
6. A method as claimed in claim 5 , wherein different language resources are assigned to at least one of different languages and different language environments.
7. A method as claimed in claim 6 , wherein different language environments indicate different environmental noise situations.
8. A method as claimed in claim 7 , further comprising reducing dimensionality of the feature vector or the language-independent feature vector sequence by a matrix multiplication before assigning to the model vector or the model vector sequence.
9. A method as claimed in claim 8 , further comprising specifying the matrix for dimensional reduction from one of a Linear Discriminant Analysis, a Principal Component Analysis and an Independent Component Analysis.
10. A method as claimed in claim 9 , wherein the speaker-independent speech recognition is language-dependent.
11. A communication device, comprising:
a microphone recording a speech signal, including a speech command, of a speaker;
a processor processing the speech signal by breaking down the speech signal into time frames, characterizing the speech signal in each captured time frame by forming a corresponding feature vector and forming a language-independent feature vector sequence from at least one feature vector;
a storage unit storing the language-independent feature vector sequence obtained from the speech signal; and
a speech recognition entity, coupled to the microphone, configured for at least speaker-dependent speech recognition by assigning the language-independent feature vector sequence to a language-dependent sequence of model vectors in a first language resource which includes a multiplicity of language-dependent model vectors, storing first assignment information which specifies assignment of the language-independent feature vector sequence to the language-dependent sequence of model vectors, recognizing the speech command which is assigned to the language-dependent sequence of model vectors, selecting a second language resource different from the first language resource, assigning the language-independent feature vector sequence previously stored to a language-dependent model vector sequence in the second language resource, and storing second assignment information corresponding thereto.
12. A communication device as claimed in claim 11 , wherein said speech recognition entity simultaneously uses speaker-dependent and speaker-independent vocabularies.