Search Results for author:
Found 16 papers, 4 papers with code
Self-Critical Alternate Learning based Semantic Broadcast Communication
In particular, to enable stable optimization via a nondifferentiable semantic metric, we regard sentence similarity as a reward and formulate this learning process as an RL problem.
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field
A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity.
ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech
Specifically, we first introduce a word-level prosody encoder, which quantizes the low-frequency band of the speech and compresses prosody attributes in the latent prosody vector (LPV).
Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual Information
In this paper, we reformulate this task as a single-label prediction problem by encoding the multi-speaker labels with power set.
FedSpeech: Federated Text-to-Speech with Continual Learning
Federated learning enables collaborative training of machine learning models under strict privacy restrictions and federated text-to-speech aims to synthesize natural speech of multiple users with a few audio training samples stored in their devices locally.
BeamTransformer: Microphone Array-based Overlapping Speech Detection
We propose BeamTransformer, an efficient architecture to leverage beamformer's edge in spatial filtering and transformer's capability in context sequence modeling.
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model
Finally, by showing a comparable performance in the emotional speech synthesis task, we successfully demonstrate the ability of the proposed model.
A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection
Linear Programming (LP) is an important decoding technique for binary linear codes.
Simplified Self-Attention for Transformer-based End-to-End Speech Recognition
Transformer models have been introduced into end-to-end speech recognition with state-of-the-art performance on various tasks owing to their superiority in modeling long-term dependencies.
Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition
Recently, streaming end-to-end automatic speech recognition (E2E-ASR) has gained more and more attention.
Sound Audio and Speech Processing
ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning
Inspired by the recent advances in deep learning (DL), this work presents a deep neural network aided decoding algorithm for binary linear codes.
Learned Conjugate Gradient Descent Network for Massive MIMO Detection
Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is integrated into the LcgNet to smartly quantize the aforementioned step-sizes.
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition
Results in a 20, 000 hours Mandarin speech recognition task show that the proposed spelling correction model can achieve a CER of 3. 41%, which results in 22. 9% and 53. 2% relative improvement compared to the baseline CTC-based systems decoded with and without language model respectively.
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
In a 20000 hours Mandarin recognition task, the LFR trained DFSMN can achieve more than 20% relative improvement compared to the LFR trained BLSTM.
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.
Data preprocessing methods for robust Fourier ptychographic microscopy
Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging technique with both high resolution and wide field-of-view.
