Speech Recognition of Partial Proper Names by Natural Language Processing

• US 20150348543A1
• Filed: 06/02/2015
• Published: 12/03/2015
• Est. Priority Date: 06/02/2014
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Zhao, Lin, Feng, Zhe, Xu, Kui, Weng, Fuliang
• Granted Patent: US 9,589,563 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G10L 15/19 Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules

Abstract

A method for speech recognition of partial proper names is described which includes natural language processing (NLP), partial name candidate generation, speech recognition and post processing. Natural language processing techniques including shallow and deep parsing are applied to long proper names to identify syntactic units (for example, noun phrases). The syntactic units form a basis for generating a candidate list of partial names for each original full name. A partial name is part of the original name, with some words omitted, or word order changed, or even word substitution. After candidate partial names are generated, their phonetic transcriptions are incorporated into a model for a speech recognizer to recognize the partial names in a speech recognition system.

Claims

1. A method of speech recognition for partial proper names comprising:

receiving with a processor a corpus including a plurality of complex proper names;

performing with the processor natural language processing to generate syntactic structure corresponding to a plurality of words in one complex proper name in the corpus;

generating with the processor a plurality of candidate partial names corresponding to the one complex proper name using a machine learning process with reference to the syntactic structure corresponding to the one complex proper name and the plurality of words in the one complex proper name;

incorporating with the processor the plurality of candidate partial names into a model for a speech recognizer to recognize partial names in a speech recognition process; and

storing with the processor the model for the speech recognizer in a memory for identification of the complex proper names in a speech recognition process after recognizing the partial names.

2. The method of claim 1 further comprising:

receiving with the processor speech input data including a partial proper name;

identifying with the processor a complex proper name corresponding to the partial proper name with reference to the model for the speech recognizer; and

generating with the processor an output including the complex proper name in response to the speech input data.

3. The method of claim 1, the natural language processing further comprising:

generating with the processor the syntactic structure using Part-of-Speech (POS) tagging.

4. The method of claim 1, the natural language processing further comprising:

generating with the processor the syntactic structure using shallow chunking.

5. The method of claim 1, the natural language processing further comprising:

generating with the processor the syntactic structure using syntactic parsing.

6. The method of claim 1, the machine learning process further comprising:

generating with the processor the plurality of partial proper name candidates using Conditional Maximum Entropy (CME).

7. The method of claim 1, the machine learning process further comprising:

generating with the processor the plurality of partial proper name candidates using a Conditional Random Field (CRF).

8. The method of claim 1, the machine learning process further comprising:

generating with the processor the plurality of partial proper name candidates using a Hidden Markov Model (HMM).

9. The method of claim 1, the machine learning process further comprising:

generating with the processor the plurality of partial proper name candidates using a neural network.

10. A speech recognition system comprising:

a memory configured to store;

a corpus including a plurality of complex proper names; and

a model for a speech recognizer; and

a processor operatively connected to the memory, the processor being configured to;

perform natural language processing to generate syntactic structure corresponding to a plurality of words in one complex proper name in the corpus;

generate a plurality of candidate partial names corresponding to the one complex proper name using a machine learning process with reference to the syntactic structure corresponding to the one complex proper name and the plurality of words in the one complex proper name;

incorporate the plurality of candidate partial names into the model for the speech recognizer to recognize partial names in a speech recognition process; and

store the model for the speech recognizer in the memory for identification of the complex proper names in a speech recognition process after recognizing the partial names.

11. The system of claim 10, the processor being further configured to:

receive speech input data including a partial proper name;

identify a complex proper name corresponding to the partial proper name with reference to the model for the speech recognizer; and

generate an output including the complex proper name in response to the speech input data.

12. The system of claim 10, the processor being further configured to:

perform the natural language processing to generate the syntactic structure using Part-of-Speech (POS) tagging.

13. The system of claim 10, the processor being further configured to:

perform the natural language processing to generate the syntactic structure using shallow chunking.

14. The system of claim 10, the processor being further configured to:

perform the natural language processing to generate the syntactic structure using syntactic parsing.

15. The system of claim 10, the processor being further configured to:

generate the plurality of partial proper name candidates using Conditional Maximum Entropy (CME).

16. The system of claim 10, the processor being further configured to:

generate the plurality of partial proper name candidates using a Conditional Random Field (CRF).

17. The system of claim 10, the processor being further configured to:

generate the plurality of partial proper name candidates using a Hidden Markov Model (HMM).

18. The system of claim 10, the processor being further configured to:

generate the plurality of partial proper name candidates using a neural network.