SPEECH RECOGNITION APPARATUS AND METHOD

• US 20130080161A1
• Filed: 09/27/2012
• Published: 03/28/2013
• Est. Priority Date: 09/27/2011
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: TORRI, Kentaro, IWATA, Kenji, UCHIHIRA, Naoshi, CHINO, Tetsuro
• Application Number:
  US13/628,818
• Publication Number:
  US 20130080161A1
• Time in Patent Office: Days
• US Class Current: 704/231
• CPC Class Codes: G10L 15/24 Speech recognition using non-acoustical features
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/63 for local operation

Abstract

According to one embodiment, a speech recognition apparatus includes following units. The service estimation unit estimates a service being performed by a user, by using non-speech information, and to generate service information. The speech recognition unit performs speech recognition on speech information in accordance with a speech recognition technique corresponding to the service information. The feature quantity extraction unit extracts a feature quantity related to the service of the user, from the speech recognition result. The service estimation unit re-estimates the service by using the feature quantity. The speech recognition unit performs speech recognition based on the re-estimation result.

Claims

1. A speech recognition apparatus comprising:

a service estimation unit configured to estimate a service being performed by a user, by using non-speech information related to a user'"'"'s service, and to generate service information indicating a content of the estimated service;

a first speech recognition unit configured to perform speech recognition on speech information provided by the user, in accordance with a speech recognition technique corresponding to the service information, and to generate a first speech recognition result; and

a feature quantity extraction unit configured to extract at least one feature quantity related to the service being performed by the user, from the first speech recognition result,

wherein the service estimation unit re-estimates the service by using the at least one feature quantity, and the first speech recognition unit performs speech recognition based on service information resulting from the re-estimation.

2. The apparatus according to claim 1, wherein the feature quantity extraction unit extracts, as the at least one feature quantity, at least one of an appearance frequency of each word contained in the first speech recognition result, a language model likelihood of the first speech recognition result, and a number of times or a rate of presence of a sequence of words absent from learning data used to create a language model for use in the first speech recognition unit.

3. The apparatus according to claim 1, further comprising a language model selection unit configured to select a language model from a plurality of predetermined language models, in accordance with the service information,

wherein the first speech recognition unit performs speech recognition using the selected language model.

4. The apparatus according to claim 3, wherein a plurality of predetermined services are described in terms of a hierarchical structure, and the language models are associated with services positioned at a terminal of the hierarchical structure, and

the language model selection unit selects a language model corresponding to the estimated service indicated by the service information.

5. The apparatus according to claim 1, further comprising:

a related service selection unit configured to select a related service to be utilized to re-estimate the service, from a plurality of predetermined services, and to generate related service information indicating the selected related service; and

a second speech recognition unit configured to perform speech recognition on the speech information in accordance with the speech recognition technique corresponding to the related service information, and to generate a second speech recognition result,

wherein the feature quantity extraction unit extracts the at least one feature quantity from the first speech recognition result and the second speech recognition result.

6. The apparatus according to claim 5, wherein the related service selection unit selects, as the related service, one of a combination of all of the plurality of services and a service specified by the non-speech information, and

the feature quantity extraction unit extracts, as a first feature quantity, a language model likelihood of the first speech recognition result, and extracts, as a second feature quantity, a language model likelihood of the second speech recognition result, the at least one feature quantity including the first feature quantity and the second feature quantity.

7. The apparatus according to claim 1, further comprising a phoneme recognition unit configured to perform phoneme recognition on the speech information and to generate a phoneme recognition result,

wherein the feature quantity extraction unit extracts the at least one feature quantity from the first speech recognition result and the phoneme recognition result.

8. The apparatus according to claim 7, wherein the feature quantity extraction unit extracts, as a first feature quantity, a acoustic model likelihood of the first speech recognition result and extracts, as a second feature quantity, a likelihood of the phoneme recognition result, the at least one feature quantity including the first feature quantity and the second feature quantity.

9. The apparatus according to claim 1, wherein the feature quantity extraction unit extracts the at least one feature quantity from the first speech recognition result and the speech information.

10. The apparatus according to claim 9, wherein the feature quantity extraction unit extracts, as a first feature quantity, at least one of an appearance frequency of each word contained in the first speech recognition result, a language model likelihood of the first speech recognition result, and a number of times or a rate of presence of a sequence of words absent from learning data used to create a language model for use in the first speech recognition unit, and extracts, as a second feature quantity, at least one of at least one of a length of the speech information and a level of ambient noise contained in the speech information, the at least one feature quantity including the first feature quantity and the second feature quantity.

11. A speech recognition method comprising:

estimating a service being performed by a user, by using non-speech information related to a user'"'"'s service, to generate service information indicating a content of the estimated service;

performing speech recognition on speech information provided by the user, in accordance with a speech recognition technique corresponding to the service information, and generating a first speech recognition result;

extracting at least one feature quantity related to the service being performed by the user, from the first speech recognition result;

re-estimating the service by using the at least one feature quantity; and

performing speech recognition based on service information resulting from the re-estimation.

12. A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:

estimating a service being performed by a user, by using non-speech information related to a user'"'"'s service, to generate service information indicating a content of the estimated service;

performing speech recognition on speech information provided by the user, in accordance with a speech recognition technique corresponding to the service information, and generating a first speech recognition result;

extracting at least one feature quantity related to the service being performed by the user, from the first speech recognition result;

re-estimating the service by using the at least one feature quantity; and

performing speech recognition based on service information resulting from the re-estimation.