Search Results for author:
Found 9 papers, 6 papers with code
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.
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.
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
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.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
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)
+2
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.
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.
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.
