CROSS-REFERENCE TO RELATED APPLICATION

This application is a National Stage application under 35 U.S.C. § 371 of International Application No. PCT/KR2019/006508, filed on May 30, 2019, the entire disclosure of which is incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to a data mining device, and a speech recognition method and system using the same. More particularly, the present disclosure relates to a data mining device for improving a dialect recognition performance and a speech recognition method and system with an improved dialect recognition performance using the same.

BACKGROUND ART

The concept of speech recognition includes the recognition, analysis, and understanding of speech uttered by a person during a conversation between the person and a machine, and various techniques have been used for speech recognition.

The speech recognition process is largely composed of three stages. First, from inputted speech, features of speech actually uttered by a person are extracted. Then, based on the extracted features of the speech, patterns are classified through a similarity measurement with a reference acoustic model. Thereafter, within the classified patterns, by using a reference language model, the features of the speech are processed as a language, and recognized as a final sentence.

Dialects of a language generally refer to variations of that language depending on a region and social factors. Dialects are a valuable cultural heritage having a unique system and history, and showing the diversity of a language. Dialects illustrate both the change and the history of a language in terms of linguistics, and the lives of people in a region, projected onto the language in terms of cultural values. Moreover, dialects are still used by people in a certain regions along with a standard dialect.

Since a speech recognition system recognizing speech uttered in a particular language is created based on the standard dialect of the corresponding language, the capability to recognize a regional dialect, that is, a dialect, is significantly reduced.

When speech including a dialect is inputted, the dialect is replaced with a standard dialect through removal of a tone noise and a phoneme noise of the dialect speech, and the replaced standard dialect is recognized. Such technology is disclosed in Korean Patent Registration No. 10-1836430 (hereinafter referred to as “Related Art 1”).

However, Related Art 1 does not disclose a solution for a situation when tones of a standard dialect and a dialect are not distinguished from each other. Moreover, since dialect speech cannot simply be changed to standard dialect speech by only removing a phoneme noise from speech including a dialect, the capability of recognizing dialect speech may be significantly reduced.

In addition, when utterance data of a customer is collected, subjected to speech recognition, and refined to have a consistent form, and a statistical language model is learned using the same, the grammar of the statistical language model of a speech recognition engine is updated. Such data refining technology is disclosed in Korean Patent Registration No. 10-1179915 (hereinafter referred to as “Related Art 2”).

However, although Related Art 2 is suitable for generation of a language model in a consistent form such as a standard dialect, it is difficult to be applied to speech recognition in which a non-standard dialect having various forms is included.

SUMMARY OF THE DISCLOSURE

An aspect of the present disclosure is to enable speech recognition of speech including a dialect, which due to the non-standard nature of the dialect has not been easily applied to a statistical language model.

Another aspect of the present disclosure is to enable dialect speech and standard dialect speech to be distinguished from each other, rather than converting the dialect speech into standard dialect speech without being recognized as it is.

Another aspect of the present disclosure is to prevent a situation in which transcription is manually performed by a person for speech data processing, which has conventionally involved the consumption of much time and expense.

In addition, an aspect of the present disclosure is to enable speech recognition which is not delayed due to the presence of dialect speech.

While this disclosure includes specific examples, it will be apparent to one of ordinary skill in the art that various changes in form and details may be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered in a descriptive sense only, and not for purposes of limitation. Therefore, the scope of the disclosure is defined not by the detailed description, but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents are to be construed as being included in the disclosure.

A speech recognition method according to an embodiment of the present disclosure is performed by a speech recognition system designed for dialect speech recognition.

The speech recognition method may include selecting speech data including a dialect from speech data, analyzing and refining the speech data including a dialect, and learning an acoustic model and a language model using the refined speech data including a dialect.

The speech recognition method according to this embodiment of the present disclosure may further include collecting speech data.

Through the collecting speech data, the speech recognition system may collect speech data of users through the users in a region in which different types of dialects are used, and through various types of speech recognition service domains.

The selecting speech data including a dialect from speech data may include removing an abnormal vocalization from the speech data, adopting the speech data including a dialect using a reliability measurement of the speech data, and obtaining transcription data from the speech data including a dialect.

The analyzing and refining the speech data including a dialect may include extracting features from the speech data including a dialect, performing similar dialect clustering on the speech data including a dialect using the extracted features, extracting a core dialect from a similar dialect cluster, and standardizing a dialect corpus using the extracted core dialect.

Through the extracting features from the speech data including a dialect, the speech recognition system may extract at least one among pronunciation string features, lexical features, domain features, and frequency features of dialect speech.

The domain features may include information on a type of an electronic apparatus providing a speech recognition service for the user, information on a region in which the electronic apparatus is located, and information on an age group of the user of the electronic apparatus.

Through the performing similar dialect clustering, the speech recognition system may measure a degree of similarity between features through weight calculation between features according to an unsupervised learning method, and may cluster a dialect having a high degree of similarity to a threshold.

Through the extracting a core dialect from a similar dialect cluster, the speech recognition system may extract n number of objects having the highest frequency features in a cluster, and may extract a core object through a feature similarity calculation with other objects in the cluster.

Through the standardizing a dialect corpus, the speech recognition system may replace an existing dialect with a core object dialect, and may perform verification through a similarity measurement between an original dialect sentence and a replaced sentence.

A data mining device according to another embodiment of the present disclosure corresponds to a component of a speech recognition system which enables the speech recognition system to recognize dialect speech.

The data mining device may be configured to include a feature extraction module extracting features from speech data including a dialect, a deep learning module performing similar dialect clustering on the speech data including a dialect using the extracted features, a core dialect extraction module extracting a core dialect from a similar dialect cluster, and a corpus standardization module standardizing a dialect corpus using the extracted core dialect.

The feature extraction module may extract at least one among pronunciation string features, lexical features, domain features, and frequency features of dialect speech.

The domain features may include information on a type of an electronic apparatus providing speech recognition service for the user, information on a region in which the electronic apparatus is located, and information on an age group of the user of the electronic apparatus.

The deep learning module may measure a degree of similarity between features through weight calculation between features according to an unsupervised learning method, and may cluster a dialect having a high degree of similarity to a threshold.

The core dialect extraction module may extract n number of objects having the highest frequency features in a cluster, and may extract a core object through a feature similarity calculation with other objects in the cluster.

The corpus standardization module may replace an existing dialect with a core object dialect, and may perform verification through similarity measurement between an original dialect sentence and a replaced sentence.

A speech recognition system according to still another embodiment of the present disclosure includes a data collection module collecting speech data, a speech data selection module selecting speech data including a dialect from collected speech data, a speech data analysis and refinement module analyzing and refining the speech data including a dialect, a model learning module learning an acoustic model and a language model using the refined speech data including a dialect, and a speech recognition engine recognizing speech using the learned acoustic model and the learned language model.

The speech data analysis and refinement module may include a feature extraction module extracting features from speech data including a dialect, a deep learning module performing similar dialect clustering on the speech data including a dialect using the extracted features, a core dialect extraction module extracting a core dialect from a similar dialect cluster, and a corpus standardization module standardizing a dialect corpus using the extracted core dialect.

According to embodiments of the present disclosure, a performance of a speech recognition system recognizing speech including a dialect may be improved.

Further, by enabling speech including a dialect to be recognized as it is, without conversion into a standard dialect, natural language processing of language using a dialect may be performed.

Furthermore, by using artificial intelligence for data mining, data refining may be performed using a semi-automatic method.

In addition, through standardization of a corpus including a dialect, the time consumed in dialect recognition may be reduced.

