38 datasets found

The DeepSpeech dataset used for evaluation of the proposed watermarking scheme.

The proposed black-box model watermarking framework for protecting the IP of ASR models.

Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

Unsupervised Pre-Training for Speech Recognition

The AISHELL-1 dataset is a Mandarin speech corpus, consisting of 178 hours of speech, with 11 domains and 400 speakers from different accent areas in China.

The dataset used for speech intelligibility prediction with DNN-based performance measures

A transformer-based Whisper Bangla ASR model

Few-shot learning evaluation of universal representations of speech

Large-scale dataset of L1 and L2 scripted and spontaneous transcripts and recordings

The dataset used in this paper is BD-4SK-ASR, an experimental dataset which is used in the first attempt in developing an ASR system for Sorani Kurdish.

The dataset used in the paper is the IWSLT2018 speech translation task, which consists of five parts: TED corpus, Speech-translation TED corpus, TED LIUM corpus, WMT18 data and...

The Wall Street Journal dataset is used for syntactic linearization. It contains a large corpus of news articles with their corresponding syntactic trees.

Language models have been commonly adopted to boost the performance of automatic speech recognition (ASR) particularly in domain adaptation tasks. Conventional way of LM...

The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can...

The dataset used in this paper is a Conv1D equipped ASR model deployed on mobile devices.

Libriheavy is a large-scale ASR corpus consisting of 50,000 hours of read English speech derived from LibriVox. To the best of our knowledge, Libriheavy is the largest...

The ATIS dataset is a benchmark dataset for spoken language understanding, consisting of audio recordings and corresponding manual transcripts about humans asking for flight...

The TEDLIUM corpus is a large-volume corpus used for speech recognition and text summarization.

The How2 dataset consists of summarizations of How2 videos taken from YouTube.

Speech summarization, which generates a text summary from speech, can be achieved by combining automatic speech recognition (ASR) and text summarization (TS).