## 1 Introduction  
Standard Automatic Speech Recognition (ASR) systems are constructed for a specific language [1]. Hence, a system receiving speech data of a different language from the target language may operate incorrectly. In fact, ideal ASR systems should be capable of recognizing speech data regardless of languages. The Language IDentification (LID) identifying a language from given input speech can be a highly reliable pre-processing of the multi-lingual ASR systems.  
The LID module generally implements two consecutive procedures: feature extraction and classification [2]. Feature extraction aims to obtain useful LID features from input speech. Then, the classification procedure identifies a language type from the features using pattern recognition approaches. Many types of features have been applied for LID [3, 4], ranging from simple features such as pitch, speed, and energy to acoustic features such as Mel Frequency Cepstral Coefficient (MFCC). For classification, pattern recognition methods such as Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have been generally used [5]. Recently, deep learning approaches were applied for LID [6].  
Among various features, acoustic ones such as MFCC efficiently represent discriminative phonemic properties of speech signals--and have thus been used as fundamental features for LID and speech recognition. However, these features may insufficiently convey language-specific information as they aim to discriminate the general tendency of phonemic information. Some attempts based on i-vector and Deep Neural Network (DNN) were introduced for the purpose of deriving further language-specific information from the acoustic features [7, 8]. Although the approaches provided superior LID performance compared to a standard MFCC-based approach, an additional training procedure is required to obtain optimal i-vector sets and DNN parameters, thus increasing the computation time and hardware capacity. This study aims to investigate another type of feature characterizing language-specific properties, considering computation complexity.  
In previous linguistics studies, some prosodic characteristics were introduced as good measures discriminating language properties [9, 10]. Several studies reported that speech rhythm efficiently represents the prosodic characteristics of speech signals [11, 12, 13, 14]. The rhythm metrics, a representative way measuring speech rhythm, are calculated from consonantal and vocalic intervals, and they characterize the fluctuation of vocalic and consonantal intervals. The studies reported that the features provide different outputs according to languages.  
In consideration of the properties of rhythm metrics, this study proposes an LID approach using the speech rhythm as a feature. For this purpose, the way to automatically extract correct rhythm metrics from speech signals should be considered first, followed by the investigation of a pattern recognition approach that is suitable for rhythm metrics.  
This paper is organized as follows. In Section 2, the conventional LID approaches and their limitations are introduced. In Section 3, rhythm metrics and their usability for LID tasks are addressed. In addition, the proposed approach regarding the automatic extraction of rhythm metrics and overall LID procedure using the features are explained. In Section 4, several experiments conducted on several corpora and their results are reported. Section 5 concludes this paper.