Search Results for author:
Found 27 papers, 6 papers with code
Prompt as Knowledge Bank: Boost Vision-language model via Structural Representation for zero-shot medical detection
Zero-shot medical detection can further improve detection performance without relying on annotated medical images even upon the fine-tuned model, showing great clinical value.
Fine-grained Preference Optimization Improves Zero-shot Text-to-Speech
In this study, we propose a fine-grained preference optimization approach (FPO) to enhance the robustness of TTS systems.
StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching
Zero-shot voice conversion (VC) aims to transfer the timbre from the source speaker to an arbitrary unseen speaker while preserving the original linguistic content.
CTEFM-VC: Zero-Shot Voice Conversion Based on Content-Aware Timbre Ensemble Modeling and Flow Matching
Zero-shot voice conversion (VC) aims to transform the timbre of a source speaker into any previously unseen target speaker, while preserving the original linguistic content.
Takin-ADA: Emotion Controllable Audio-Driven Animation with Canonical and Landmark Loss Optimization
We discovered that these issues stem from limitations in motion representation and the lack of fine-grained control over facial expressions.
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling
Expressive zero-shot voice conversion (VC) is a critical and challenging task that aims to transform the source timbre into an arbitrary unseen speaker while preserving the original content and expressive qualities.
Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models
In this report, we introduce Takin AudioLLM, a series of techniques and models, mainly including Takin TTS, Takin VC, and Takin Morphing, specifically designed for audiobook production.
MUSA: Multi-lingual Speaker Anonymization via Serial Disentanglement
Meanwhile, we propose a straightforward anonymization strategy that employs empty embedding with zero values to simulate the speaker identity concealment process, eliminating the need for conversion to a pseudo-speaker identity and thereby reducing the complexity of speaker anonymization process.
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and Integration
Interpreting complex deep networks, notably pre-trained vision-language models (VLMs), is a formidable challenge.
GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognition
The continuous evolution of pre-trained speech models has greatly advanced Speech Emotion Recognition (SER).
PSCodec: A Series of High-Fidelity Low-bitrate Neural Speech Codecs Leveraging Prompt Encoders
In this paper, we propose PSCodec, a series of neural speech codecs based on prompt encoders, comprising PSCodec-Base, PSCodec-DRL-ICT, and PSCodec-CasAN, which are capable of delivering high-performance speech reconstruction with low bandwidths.
PP-MeT: a Real-world Personalized Prompt based Meeting Transcription System
Speaker-attributed automatic speech recognition (SA-ASR) improves the accuracy and applicability of multi-speaker ASR systems in real-world scenarios by assigning speaker labels to transcribed texts.
PromptVC: Flexible Stylistic Voice Conversion in Latent Space Driven by Natural Language Prompts
In this study, we propose PromptVC, a novel style voice conversion approach that employs a latent diffusion model to generate a style vector driven by natural language prompts.
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition
Despite notable progress, speech emotion recognition (SER) remains challenging due to the intricate and ambiguous nature of speech emotion, particularly in wild world.
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition
Contrastive cross-modality pretraining has recently exhibited impressive success in diverse fields, whereas there is limited research on their merits in speech emotion recognition (SER).
Self-Enhancement Improves Text-Image Retrieval in Foundation Visual-Language Models
The emergence of cross-modal foundation models has introduced numerous approaches grounded in text-image retrieval.
Long term 5G network traffic forecasting via modeling non-stationarity with deep learning
However, long term prediction horizon exposes the non-stationarity of series data, which deteriorates the performance of existing approaches.
Decom--CAM: Tell Me What You See, In Details! Feature-Level Interpretation via Decomposition Class Activation Map
Although the Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting object location, it fails to provide insight into the salient features used by the model to make decisions.
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism
SqueezeFormer has recently shown impressive performance in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
LMEC: Learnable Multiplicative Absolute Position Embedding Based Conformer for Speech Recognition
This paper proposes a Learnable Multiplicative absolute position Embedding based Conformer (LMEC).
Improving fairness in speaker verification via Group-adapted Fusion Network
This is observed especially with underrepresented demographic groups sharing similar voice characteristics.
Self-supervised Speaker Recognition Training Using Human-Machine Dialogues
Speaker recognition, recognizing speaker identities based on voice alone, enables important downstream applications, such as personalization and authentication.
ASR-Aware End-to-end Neural Diarization
We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Fast query-by-example speech search using separable model
Second, we design a novel model inference scheme based on RepVGG which can efficiently improve the QbE search quality.
Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets
Second, a scoring function is applied between a runtime utterance and each speaker profile.
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control
SDQL exploits the linear stage structure by approximating the Q function via a collection of deep Q sub-networks stacking along an axis marking the stage-wise progress of the whole task.
Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning
Equipping active colloidal robots with intelligence such that they can efficiently navigate in unknown complex environments could dramatically impact their use in emerging applications like precision surgery and targeted drug delivery.
