Search Results for author:
Found 9 papers, 6 papers with code
Toward Joint Language Modeling for Speech Units and Text
However, in the field of language modeling, very little effort has been made to model them jointly.
Few-Shot Spoken Language Understanding via Joint Speech-Text Models
Recent work on speech representation models jointly pre-trained with text has demonstrated the potential of improving speech representations by encoding speech and text in a shared space.
AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement
In this work, we introduce AV2Wav, a resynthesis-based audio-visual speech enhancement approach that can generate clean speech despite the challenges of real-world training data.
Layer-wise Analysis of a Self-supervised Speech Representation Model
Recently proposed self-supervised learning approaches have been successful for pre-training speech representation models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization
Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-target scenario in which a single model is trained to convert the input voice to many different speakers.
Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences
In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data.
Sound Audio and Speech Processing
Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations
The decoder then takes the speaker-independent latent representation and the target speaker embedding as the input to generate the voice of the target speaker with the linguistic content of the source utterance.
Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks
Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition.
