A Syllable Structure Approach to Spoken Language Recognition

Abstract

Spoken language recognition is the task of automatically determining the identity of the language spoken in a speech clip. Prior approaches to spoken language recognition have been able to accurately determine the language within an audio clip. However, they usually require long training time and large datasets since most of the existing approaches heavily rely on phonotactic, acoustic-phonetic and prosodic information. Moreover, the features extracted may not be linguistic features, but speaker features instead. This paper presents a novel approach based on a linguistics perspective, particularly that of syllable structure. Based on human listening experiments, there has been strong evidence that syllable structure is a significant knowledge source in human spoken language recognition. The approach includes a block for labelling common syllable structures (CV, CVC, VC, etc.). Then, a long short-term memory (LSTM) network is used to transform the Mel-frequency cepstral coefficients (MFCC) of an audio clip to its syllable structure, thereby diminishing the influence of speakers on extracted features and reducing the number of dimensions for the final language predictor. The array of syllables is then passed through the second LSTM network to predict the language. The proposed method creates a generalized and scalable framework with acceptable accuracy for spoken language recognition. Our experiments with 10 different languages demonstrate the feasibility of the proposed approach, which achieves a comparable accuracy of 70.40% with a computing time of 37 ms for every second of speech, outperforming most of the existing methods based on acoustic-phonetic and phonotactic features by efficiency.