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Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition.

, and . INTERSPEECH, page 3228-3232. ISCA, (2019)

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The USFD SLT system for IWSLT 2014., , , , , , , , , and . IWSLT (Evaluation Campaign), (2014)Background-tracking Acoustic Features for Genre Identification of Broadcast Shows., , and . CoRR, (2015)Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition., and . INTERSPEECH, page 3228-3232. ISCA, (2019)Recurrent Neural Network Language Model Adaptation for Multi-Genre Broadcast Speech Recognition and Alignment., , , , and . IEEE ACM Trans. Audio Speech Lang. Process., 27 (3): 572-582 (2019)Unsupervised domain discovery using latent dirichlet allocation for acoustic modelling in speech recognition., , and . INTERSPEECH, page 3640-3644. ISCA, (2015)The Sheffield language recognition system in NIST LRE 2015., , , , , , , and . Odyssey, page 181-187. ISCA, (2016)Combining Feature and Model-Based Adaptation of RNNLMs for Multi-Genre Broadcast Speech Recognition., , , , and . INTERSPEECH, page 2343-2347. ISCA, (2016)Automatic Genre and Show Identification of Broadcast Media., , , and . INTERSPEECH, page 2115-2119. ISCA, (2016)Background-tracking acoustic features for genre identification of broadcast shows., , and . SLT, page 118-123. IEEE, (2014)Long-Term Statistical Feature Extraction from Speech Signal and Its Application in Emotion Recognition., , , and . SLSP, volume 9449 of Lecture Notes in Computer Science, page 173-184. Springer, (2015)