Spoken Language Identification Using Bidirectional LSTM Based LID Sequential Senones

The effectiveness of features used to represent speech utterances influences the performance of spoken language identification (LID) systems. Recent LID systems use bottleneck features (BNFs) obtained from deep neural networks (DNNs) to represent the utterances. These BNFs do not encode language-specific features. The recent advances in DNNs have led to the usage of effective language-sensitive features such as LID-senones, obtained using convolutional neural network (CNN) based architecture. In this work, we propose a novel approach to obtain LID-senones. The proposed approach combines BNF with bidirectional long short-term memory (BLSTM) networks to generate LID-senones. Since each LID-senones preserve sequence information, we term it as LID-sequential-senones (LID-seq-senones). The proposed LID-seq-senones are then used for LID in two ways. In the first approach, we propose to build an end-to-end structure with BLSTM as front end LID-seq-senones extractor followed by a fully connected classification layer. In the second approach, we consider each utterance as a sequence of LID-seq-senones and propose to use support vector machine (SVM) with sequence kernel (GMM-based segment level pyramid match kernel) to classify the utterance. The effectiveness of proposed representation is evaluated on Oregon graduate institute multi-language telephone speech corpus (OGI-TS) and IIT Madras Indian language corpus (IITM-IL).