Search Results for author:
Found 9 papers, 1 papers with code
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time.
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system.
A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography
Doppler echocardiography offers critical insights into cardiac function and phases by quantifying blood flow velocities and evaluating myocardial motion.
Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features
In echocardiographic view classification, accurately detecting out-of-distribution (OOD) data is essential but challenging, especially given the subtle differences between in-distribution and OOD data.
A comparison of streaming models and data augmentation methods for robust speech recognition
However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques.
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages
To improve the accuracy of a low-resource Italian ASR, we leverage a well-trained English model, unlabeled text corpus, and unlabeled audio corpus using transfer learning, TTS augmentation, and SSL respectively.
A review of on-device fully neural end-to-end automatic speech recognition algorithms
Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder based on a Weighted Finite State Transducer (WFST), and so on.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
end-to-end training of a large vocabulary end-to-end speech recognition system
Our end-to-end speech recognition system built using this training infrastructure showed a 2. 44 % WER on test-clean of the LibriSpeech test set after applying shallow fusion with a Transformer language model (LM).
Deep ensemble network with explicit complementary model for accuracy-balanced classification
The average accuracy is one of major evaluation metrics for classification systems, while the accuracy deviation is another important performance metric used to evaluate various deep neural networks.
