LID-Senones and Their Statistics for Language Identification
By Ma Jin, Yan Song, Ian Vince McLoughlin, Lirong Dai. 2018
21 citations recorded by the source corpus.
Recent research on end-to-end training structures for language identification has raised the possibility that intermediate language-sensitive feature units exist which are analogous to phonetically sensitive senones in automatic speech recognition systems. Termed language identification (LID)-senones, the statistics derived from these feature units have been shown to be beneficial in discriminating between languages, particularly for short utterances. This paper examines the evidence for the existence of LID-senones before designing and evaluating LID systems based on low- and high-level statistics of LID-senones with both generative and discriminative models. For the standard NIST LRE 2009 task on 23 languages, LID-senone-based systems are shown to outperform state-of-the-art deep neural network/i-vector methods both when LID-senones are used directly for classification and when LID-senone statistics are used for i-vector formation.
Subfields: Artificial Intelligence, Signal Processing
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In Artificial Intelligence
- Weicheng Cai
- Jinkun Chen
- Ming Li
- Joon Son Chung
- Andrew Zisserman
- Arsha Nagrani
- Weidi Xie
- Junichi Yamagishi
In Signal Processing
Citations in the corpus
- Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System (2018, background): “In recent years, with the merit of large labeled datasets, enormous computation capability, and effective network architectures, emerging progress towards end-to-end learning opens up a new area for exploration [19, 20, 21, 22, 23].”