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Information in speech can be divided into two categories: what is being said (content) and how it is expressed (other). Current state-of-the-art (SOTA) techniques model speech at fixed segments, usually 10-25 ms, using a single embedding. Given the o
Externí odkaz:
http://arxiv.org/abs/2410.11086
Speech modeling methods learn one embedding for a fixed segment of speech, typically in between 10-25 ms. The information present in speech can be divided into two categories: "what is being said" (content) and "how it is expressed" (other) and these
Externí odkaz:
http://arxiv.org/abs/2408.10557
In recent years, self-supervised pre-training methods have gained significant traction in learning high-level information from raw speech. Among these methods, HuBERT has demonstrated SOTA performance in automatic speech recognition (ASR). However, H
Externí odkaz:
http://arxiv.org/abs/2406.05661
This paper introduces a novel objective function for quality mean opinion score (MOS) prediction of unseen speech synthesis systems. The proposed function measures the similarity of relative positions of predicted MOS values, in a mini-batch, rather
Externí odkaz:
http://arxiv.org/abs/2310.05078
Recent developments in pre-trained speech representation utilizing self-supervised learning (SSL) have yielded exceptional results on a variety of downstream tasks. One such technique, known as masked predictive coding (MPC), has been employed by som
Externí odkaz:
http://arxiv.org/abs/2303.06982
Autor:
Yadav, Hemant, Sitaram, Sunayana
Although Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, the majority of the world's languages do not have usable systems due to the lack of large speech datasets to train these models. Cross-lingu
Externí odkaz:
http://arxiv.org/abs/2202.12576
Building Spoken Language Understanding (SLU) systems that do not rely on language specific Automatic Speech Recognition (ASR) is an important yet less explored problem in language processing. In this paper, we present a comparative study aimed at emp
Externí odkaz:
http://arxiv.org/abs/2110.09264
Publikováno v:
In Oil Crop Science July 2024 9(3):151-159
Autor:
Yadav, Hemant, Thakkar, Amit
Publikováno v:
In Expert Systems With Applications 15 March 2024 238 Part F