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检索条件"机构=Machine Learning and Human Language Technology"
40 条 记 录,以下是1-10 订阅
排序:
Right Label Context in End-to-End Training of Time-Synchronous ASR Models
Right Label Context in End-to-End Training of Time-Synchrono...
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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
作者: Raissi, Tina Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology Group RWTH Aachen University Germany AppTek GmbH Germany
Current time-synchronous sequence-to-sequence automatic speech recognition (ASR) models are trained by using sequence level cross-entropy that sums over all alignments. Due to the discriminative formulation, incorpora... 详细信息
来源: 评论
Right Label Context in End-to-End Training of Time-Synchronous ASR Models
Right Label Context in End-to-End Training of Time-Synchrono...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Tina Raissi Ralf Schlüter Hermann Ney Machine Learning and Human Language Technology Group RWTH Aachen University AppTek GmbH Germany
Current time-synchronous sequence-to-sequence automatic speech recognition (ASR) models are trained by using sequence level cross-entropy that sums over all alignments. Due to the discriminative formulation, incorpora... 详细信息
来源: 评论
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression
arXiv
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arXiv 2025年
作者: Xu, Jingjing Beck, Eugen Yang, Zijian Schlüter, Ralf Machine Learning and Human Language Technology Group RWTH Aachen University Germany AppTek GmbH Germany
ASR systems are deployed across diverse environments, each with specific hardware constraints. We use supernet training to jointly train multiple encoders of varying sizes, enabling dynamic model size adjustment to fi... 详细信息
来源: 评论
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression
Efficient Supernet Training with Orthogonal Softmax for Scal...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Jingjing Xu Eugen Beck Zijian Yang Ralf Schlüter Machine Learning and Human Language Technology Group RWTH Aachen University Germany AppTek GmbH Germany
ASR systems are deployed across diverse environments, each with specific hardware constraints. We use supernet training to jointly train multiple encoders of varying sizes, enabling dynamic model size adjustment to fi... 详细信息
来源: 评论
Right Label Context in End-to-End Training of Time-Synchronous ASR Models
arXiv
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arXiv 2025年
作者: Raissi, Tina Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology Group RWTH Aachen University Germany AppTek GmbH Germany
Current time-synchronous sequence-to-sequence automatic speech recognition (ASR) models are trained by using sequence level cross-entropy that sums over all alignments. Due to the discriminative formulation, incorpora... 详细信息
来源: 评论
Classification Error Bound for Low Bayes Error Conditions in machine learning
arXiv
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arXiv 2025年
作者: Yang, Zijian Eminyan, Vahe Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 Computer Science Department RWTH Aachen University Germany AppTek GmbH Germany
In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknown true distribution is usually replac... 详细信息
来源: 评论
Classification Error Bound for Low Bayes Error Conditions in machine learning
Classification Error Bound for Low Bayes Error Conditions in...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Zijian Yang Vahe Eminyan Ralf Schlüter Hermann Ney Computer Science Department Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 RWTH Aachen University Germany AppTek GmbH Germany
In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknown true distribution is usually replac... 详细信息
来源: 评论
Leveraging Cross-Lingual Transfer learning in Spoken Named Entity Recognition Systems  20
Leveraging Cross-Lingual Transfer Learning in Spoken Named E...
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20th Conference on Natural language Processing, KONVENS 2024
作者: Benaicha, Moncef Thulke, David Tuğtekin Turan, M.A. Germany Machine Learning and Human Language Technology RWTH Aachen University Germany
Recent Named Entity Recognition (NER) advancements have significantly enhanced text classification capabilities. This paper focuses on spoken NER, aimed explicitly at spoken document retrieval, an area not widely stud... 详细信息
来源: 评论
Comparative Analysis of the wav2vec 2.0 Feature Extractor  15
Comparative Analysis of the wav2vec 2.0 Feature Extractor
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15th ITG Conference on Speech Communication
作者: Vieting, Peter Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology RWTH Aachen University Germany AppTek GmbH Germany
Automatic speech recognition (ASR) systems typically use handcrafted feature extraction pipelines. To avoid their inherent information loss and to achieve more consistent modeling from speech to transcribed text, neur... 详细信息
来源: 评论
Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization  2
Prompting and Fine-Tuning of Small LLMs for Length-Controlla...
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2nd International Conference on Foundation and Large language Models, FLLM 2024
作者: Thulke, David Gao, Yingbo Jalota, Rricha Dugast, Christian Ney, Hermann AppTek GmbH Aachen Germany RWTH Aachen University Machine Learning and Human Language Technology Group Germany
This paper explores the rapid development of a telephone call summarization system utilizing large language models (LLMs). Our approach involves initial experiments with prompting existing LLMs to generate summaries o... 详细信息
来源: 评论