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Found 7 papers, 0 papers with code
Text-guided HuBERT: Self-Supervised Speech Pre-training via Generative Adversarial Networks
In this paper, we investigate a new way to pre-train such a joint speech-text model to learn enhanced speech representations and benefit various speech-related downstream tasks.
CoAVT: A Cognition-Inspired Unified Audio-Visual-Text Pre-Training Model for Multimodal Processing
To bridge the gap between modalities, CoAVT employs a query encoder, which contains a set of learnable query embeddings, and extracts the most informative audiovisual features of the corresponding text.
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder
We train the model based on the idea that different realisations of the same word should be close in the underlying embedding space.
Self-Transriber: Few-shot Lyrics Transcription with Self-training
The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive.
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text
Firstly, due to the distinct characteristics between speech and text modalities, where speech is continuous while text is discrete, we first discretize speech into a sequence of discrete speech tokens to solve the modality mismatch problem.
End-to-End Code-Switching ASR for Low-Resourced Language Pairs
In this work, we describe an E2E ASR pipeline for the recognition of CS speech in which a low-resourced language is mixed with a high resourced language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-Graph Decoding for Code-Switching ASR
This archive contains recordings with monolingual Frisian and Dutch speech segments as well as Frisian-Dutch CS speech, hence the recognition performance on monolingual segments is also vital for accurate transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
