Method for recognizing speech

• US 20040167778A1
• Filed: 02/18/2004
• Published: 08/26/2004
• Est. Priority Date: 02/20/2003
• Status: Abandoned Application

• Current Assignee: Sony Deutschland GmbH (Sony Group Corp.)
• Original Assignee: Sony Deutschland GmbH (Sony Group Corp.)
• Inventors: Valsan, Zica, Emele, Martin
• Application Number:
  US10/781,521
• Publication Number:
  US 20040167778A1
• Time in Patent Office: Days
• US Class Current: 704/231
• CPC Class Codes: G10L 15/16 using artificial neural networks
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/19 Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules

Abstract

The present invention relates to a method for recognizing speech, which method leads to an improved recognition rate compared to prior art. Within the method a language model is applied which is based on attribute information of a word, which is descriptive for syntactic and/or semantic information and/or the like of the respective word. The method for recognizing speech according to the invention, comprises the steps of receiving (S0) a speech input (SI), generating (S1) a set of ordered hypotheses (OH), wherein each hypothesis contains at least one hypothesis word, generating (S2) attribute information (AI) for at least one of said at least one hypothesis word, the attribute information being generated to be descriptive for syntactic and/or semantic information and/or the like of the respective hypothesis word, using (S3) a language model (LM) which is based on said attribute information (AI) to calculate word probabilities for said at least one of said at least one hypothesis word, which word probabilities are descriptive for the posterior probabilities of the respective hypothesis word given a plurality of previous hypothesis words, using (S4) said word probabilities for generating a set of re-ordered hypotheses (ROH), choosing (S5) at least one best hypothesis (BH) from said set of re-ordered hypotheses (ROH) as a recognition result (RR), and outputting (S6) said recognition result.

Claims

1. Method for recognizing speech,

comprising the steps of

receiving (S0) a speech input (SI),

generating (SI) a set of ordered hypotheses (OH), wherein each hypothesis contains at least one hypothesis word,

generating (S2) attribute information (Al) for at least one of said at least one hypothesis word, the attribute information being generated to be descriptive for syntactic and/or semantic information and/or the like of a respective hypothesis word,

using (S3) a language model (LM) which is based on said attribute information (AI) to calculate word probabilities for said at least one of said at least one hypothesis word, which word probabilities are descriptive for the posterior probabilities of the respective hypothesis word given a plurality of previous hypothesis words,

using (S4) said word probabilities for generating a set of re-ordered hypotheses (ROH),

choosing (S5) at least one best hypothesis (BH) from said set of re-ordered hypotheses (ROH) as a recognition result (RR),

outputting (S6) said recognition result.

2. The method according to claim 1,

characterized by

generating said attribute information (AI) for a combination of hypothesis words, wherein the attribute information (AI) is descriptive for syntactic and/or semantic information and/or the like of the combination of hypothesis words.

3. The method according to any one of the preceding claims,

characterized in that

said word probabilities are determined using a trainable probability estimator (TPE), in particular an artificial neural network (ANN).

4. The method according to claim 3,

characterized in that

said artificial neural network (ANN) is a time delay neural network, a recurrent neural network or a multilayer perceptron network.

5. The method according to claims 3 or 4,

characterized by

generating a feature vector (FV) that is used as input for said trainable probability estimator (TPE), which feature vector (FV) contains coded attribute information.

6. The method according to claim 5,

characterized by

applying a method for dimensionality reduction to the feature vector (FV).

7. The method according to claim 6,

characterized in that

said method for dimensionality reduction is based on principal component analysis, latent semantic indexing, and/or random mapping projection (RMP).

8. The method according to any one of the preceding claims,

characterized in that

a standard language model is applied additionally to said language model (LM).

9. Speech processing system,

which is capable of performing or realizing a method for recognizing speech according to any one of the preceding claims 1 to 8 and/or the steps thereof.

10. Computer program product,

comprising computer program means adapted to perform and/or to realize the method of recognizing speech according to any one of the claims 1 to 8 and/or the steps thereof, when it is executed on a computer, a digital signal processing means, and/or the like.

11. Computer readable storage medium,

comprising a computer program product according to claim 10.