End-to-End Language Identification Using High-Order Utterance Representation with Bilinear Pooling

Ma Jin, Yan Song, Ian McLoughlin, Wu Guo, Li-Rong Dai
2017 Interspeech 2017   unpublished
A key problem in spoken language identification (LID) is how to design effective representations which are specific to language information. Recent advances in deep neural networks have led to significant improvements in results, with deep endto-end methods proving effective. This paper proposes a novel network which aims to model an effective representation for high (first and second)-order statistics of LID-senones, defined as being LID analogues of senones in speech recognition. The
more » ... ition. The high-order information extracted through bilinear pooling is robust to speakers, channels and background noise. Evaluation with NIST LRE 2009 shows improved performance compared to current state-of-the-art DBF/i-vector systems, achieving over 33% and 20% relative equal error rate (EER) improvement for 3s and 10s utterances and over 40% relative Cavg improvement for all durations.
doi:10.21437/interspeech.2017-44 fatcat:cn7ntwf24fhfvnjvp3ijmet5mq