Search Results for author:
Found 29 papers, 8 papers with code
Unispeaker: A Unified Approach for Multimodality-driven Speaker Generation
Recent advancements in personalized speech generation have brought synthetic speech increasingly close to the realism of target speakers' recordings, yet multimodal speaker generation remains on the rise.
FlashAudio: Rectified Flows for Fast and High-Fidelity Text-to-Audio Generation
Recent advancements in latent diffusion models (LDMs) have markedly enhanced text-to-audio generation, yet their iterative sampling processes impose substantial computational demands, limiting practical deployment.
IntrinsicVoice: Empowering LLMs with Intrinsic Real-time Voice Interaction Abilities
Current methods of building LLMs with voice interaction capabilities rely heavily on explicit text autoregressive generation before or during speech response generation to maintain content quality, which unfortunately brings computational overhead and increases latency in multi-turn interactions.
Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives
Deep learning has gained significant attention in remote sensing, especially in pixel- or patch-level applications.
CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens
Based on the tokens, we further propose a scalable zero-shot TTS synthesizer, CosyVoice, which consists of an LLM for text-to-token generation and a conditional flow matching model for token-to-speech synthesis.
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models
Deep reinforcement learning (DRL) has achieved remarkable success across various domains, such as video games, robotics, and, recently, large language models.
FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs
This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs).
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).
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.
DiaCorrect: Error Correction Back-end For Speaker Diarization
In this work, we propose an error correction framework, named DiaCorrect, to refine the output of a diarization system in a simple yet effective way.
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.
METTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer
However, such data-efficient approaches have ignored synthesizing emotional aspects of speech due to the challenges of cross-speaker cross-lingual emotion transfer - the heavy entanglement of speaker timbre, emotion, and language factors in the speech signal will make a system produce cross-lingual synthetic speech with an undesired foreign accent and weak emotion expressiveness.
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).
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).
DiaCorrect: End-to-end error correction for speaker diarization
In recent years, speaker diarization has attracted widespread attention.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving Cross-lingual Speech Synthesis with Triplet Training Scheme
Recent advances in cross-lingual text-to-speech (TTS) made it possible to synthesize speech in a language foreign to a monolingual speaker.
The USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party meeting transcription (M2MeT) challenge
We propose two improvements to target-speaker voice activity detection (TS-VAD), the core component in our proposed speaker diarization system that was submitted to the 2022 Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenge.
Imaging vibrations of locally gated, electromechanical few layer graphene resonators with a moving vacuum enclosure
With it we characterize the lowest frequency mode of a FLG resonator by measuring its frequency response as a function of position on the membrane.
Mesoscale and Nanoscale Physics
Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training
In order to make timbre conversion more stable and controllable, speaker embedding is further decomposed to the weighted sum of a group of trainable vectors representing different timbre clusters.
TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis
On one hand, we propose to discriminate ground-truth waveform from synthetic one in frequency domain for offering more consistency guarantees instead of only in time domain.
Peking Opera Synthesis via Duration Informed Attention Network
In this work, we propose to deal with this issue and synthesize expressive Peking Opera singing from the music score based on the Duration Informed Attention Network (DurIAN) framework.
AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN
This paper investigates how to leverage a DurIAN-based average model to enable a new speaker to have both accurate pronunciation and fluent cross-lingual speaking with very limited monolingual data.
Synthesising Expressiveness in Peking Opera via Duration Informed Attention Network
This paper presents a method that generates expressive singing voice of Peking opera.
Learning Singing From Speech
The proposed algorithm first integrate speech and singing synthesis into a unified framework, and learns universal speaker embeddings that are shareable between speech and singing synthesis tasks.
PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network
However, the converted singing voice can be easily out of key, showing that the existing approach cannot model the pitch information precisely.
DurIAN: Duration Informed Attention Network For Multimodal Synthesis
In this paper, we present a generic and robust multimodal synthesis system that produces highly natural speech and facial expression simultaneously.
Deep Feed-forward Sequential Memory Networks for Speech Synthesis
The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among the best parametric Text-to-Speech (TTS) systems in terms of the naturalness of generated speech, especially the naturalness in prosody.
