Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Shon, Suwon"'
Reference summaries for abstractive speech summarization require human annotation, which can be performed by listening to an audio recording or by reading textual transcripts of the recording. In this paper, we examine whether summaries based on anno
Externí odkaz:
http://arxiv.org/abs/2408.07277
Autor:
Arora, Siddhant, Pasad, Ankita, Chien, Chung-Ming, Han, Jionghao, Sharma, Roshan, Jung, Jee-weon, Dhamyal, Hira, Chen, William, Shon, Suwon, Lee, Hung-yi, Livescu, Karen, Watanabe, Shinji
The Spoken Language Understanding Evaluation (SLUE) suite of benchmark tasks was recently introduced to address the need for open resources and benchmarking of complex spoken language understanding (SLU) tasks, including both classification and seque
Externí odkaz:
http://arxiv.org/abs/2406.10083
The integration of pre-trained text-based large language models (LLM) with speech input has enabled instruction-following capabilities for diverse speech tasks. This integration requires the use of a speech encoder, a speech adapter, and an LLM, trai
Externí odkaz:
http://arxiv.org/abs/2406.09345
When performing tasks like automatic speech recognition or spoken language understanding for a given utterance, access to preceding text or audio provides contextual information can improve performance. Considering the recent advances in generative l
Externí odkaz:
http://arxiv.org/abs/2312.09895
Autor:
Peng, Yifan, Kim, Kwangyoun, Wu, Felix, Yan, Brian, Arora, Siddhant, Chen, William, Tang, Jiyang, Shon, Suwon, Sridhar, Prashant, Watanabe, Shinji
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spo
Externí odkaz:
http://arxiv.org/abs/2305.11073
Autor:
Shon, Suwon, Arora, Siddhant, Lin, Chyi-Jiunn, Pasad, Ankita, Wu, Felix, Sharma, Roshan, Wu, Wei-Lun, Lee, Hung-Yi, Livescu, Karen, Watanabe, Shinji
Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like speech and speaker recognition. In particular, there are not nearly as ma
Externí odkaz:
http://arxiv.org/abs/2212.10525
Self-supervised pre-trained transformers have improved the state of the art on a variety of speech tasks. Due to the quadratic time and space complexity of self-attention, they usually operate at the level of relatively short (e.g., utterance) segmen
Externí odkaz:
http://arxiv.org/abs/2212.08542
Spoken language understanding (SLU) tasks involve mapping from speech audio signals to semantic labels. Given the complexity of such tasks, good performance might be expected to require large labeled datasets, which are difficult to collect for each
Externí odkaz:
http://arxiv.org/abs/2112.07648
Autor:
Shon, Suwon, Pasad, Ankita, Wu, Felix, Brusco, Pablo, Artzi, Yoav, Livescu, Karen, Han, Kyu J.
Progress in speech processing has been facilitated by shared datasets and benchmarks. Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks. Interest has been growing in higher-level
Externí odkaz:
http://arxiv.org/abs/2111.10367
In this paper, we explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis. First, we investigate how useful a pre-trained language model would be in a 2-step pipeline approach emplo
Externí odkaz:
http://arxiv.org/abs/2106.06598