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Identifying Mood Episodes Using Dialogue Features from Clinical Interviews.

, , , , and . INTERSPEECH, page 1926-1930. ISCA, (2019)

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Recent advances in conversational speech recognition using convolutional and recurrent neural networks., and . IBM J. Res. Dev., 61 (4-5): 1:1-1:10 (2017)AVLnet: Learning Audio-Visual Language Representations from Instructional Videos., , , , , , , , , and 4 other author(s). Interspeech, page 1584-1588. ISCA, (2021)Identifying Mood Episodes Using Dialogue Features from Clinical Interviews., , , , and . INTERSPEECH, page 1926-1930. ISCA, (2019)A Highly Efficient Distributed Deep Learning System for Automatic Speech Recognition., , , , , , , , and . INTERSPEECH, page 2628-2632. ISCA, (2019)Impact of bucketing on performance of linearly interpolated language models., , and . INTERSPEECH, page 178-181. ISCA, (2000)Diarization of Legal Proceedings. Identifying and Transcribing Judicial Speech from Recorded Court Audio., , , , , , and . CoRR, (2021)Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions., , , , , , , , , and . CoRR, (2021)AVLnet: Learning Audio-Visual Language Representations from Instructional Videos., , , , , , , , , and 1 other author(s). CoRR, (2020)Distributed Deep Learning Strategies for Automatic Speech Recognition., , , , , , and . ICASSP, page 5706-5710. IEEE, (2019)Concept-based speech-to-speech translation using maximum entropy models for statistical natural concept generation., , , and . IEEE Trans. Speech Audio Process., 14 (2): 377-392 (2006)