A comparison of cepstral and spectral features using recurrent neural network for spoken language identification

Abstract

Spoken language identification is the process of confirming labels regarding the language of an audio slice regardless of various features such as length, ambiance, duration, topic or message, age, gender, region, emotions, etc. Language identification systems are of great significance in the domain of natural language processing, more specifically multi-lingual machine translation, language recognition, and automatic routing of voice calls to particular nodes speaking or knowing a particular language. In his paper, we are comparing results based on various cepstral and spectral feature techniques such as Mel-frequency Cepstral Coefficients (MFCC), Relative spectral-perceptual linear prediction coefficients (RASTA-PLP), and spectral features (roll-off, flatness, centroid, bandwidth, and contrast) in the process of spoken language identification using Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) as a procedure of sequence learning. The system or model has been implemented in six different languages, which contain Ladakhi and the five official languages of Jammu and Kashmir (Union Territory). The dataset used in experimentation consists of TV audio recordings for Kashmiri, Urdu, Dogri, and Ladakhi languages. It also consists of standard corpora IIIT-H and VoxForge containing English and Hindi audio data. Pre-processing of the dataset is done by slicing different types of noise with the use of the Spectral Noise Gate (SNG) and then slicing into audio bursts of 5 seconds duration. The performance is evaluated using standard metrics like F1 score, recall, precision, and accuracy. The experimental results showed that using spectral features, MFCC and RASTA-PLP achieved an average accuracy of 76%, 83%, and 78%, respectively. Therefore, MFCC proved to be the most convenient feature to be exploited in language identification using a recurrent neural network long short-term memory classifier.