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Chenfeng Miao

7 accepted papers

2025

EffectiveASR: A Single-Step Non-Autoregressive Mandarin Speech Recognition Architecture with High Accuracy and Inference Speed

ICASSP 2025accepted

Non-autoregressive (NAR) automatic speech recognition (ASR) models predict tokens independently and simultaneously, bringing high inference speed. However, there is still a gap in the accuracy of the NAR models compared to the autoregressive (AR) models. In this paper, we propose a single-step NAR A…

Cited by 0SourceScholar
2025

LEF-TTS: Lightweight and Efficient End-to-End Text-to-Speech Synthesis With Multi-Stream Generator

ICASSP 2025accepted

Recently, the field of Text-to-speech synthesis has been predominantly characterized by end-to-end models, with the quality of speech generated by these models becoming increasingly comparable to that of human speech. In this work, we propose a Lightweight and Efficient Text-to-speech model, a fast…

Cited by 0SourceScholar
2024

DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation

ICML 2024poster

In this work, we present DFlow, a novel generative framework that combines Normalizing Flow (NF) with a Denoising AutoEncoder (DAE), for high-fidelity waveform generation. With a tactfully designed structure, DFlow seamlessly integrates the capabilities of both NF and DAE, resulting in a significant…

Cited by 0SourcePDFScholar
2024

Improving Attention-Based End-to-End Speech Recognition by Monotonic Alignment Attention Matrix Reconstruction

ICASSP 2024accepted

In automatic speech recognition (ASR) task, the output sequence should correspond to a linear transcription of the input sequence. Lots of works have been done to learn the monotonic alignment in end-to-end (E2E) ASR model, but their methods mainly focus on streaming propose and usually result in a…

Cited by 0SourceScholar
2021

EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture

ICML 2021spotlight

In this work, we address the Text-to-Speech (TTS) task by proposing a non-autoregressive architecture called EfficientTTS. Unlike the dominant non-autoregressive TTS models, which are trained with the need of external aligners, EfficientTTS optimizes all its parameters with a stable, end-to-end trai…

2021

Unsupervised Learning for Multi-Style Speech Synthesis with Limited Data

ICASSP 2021accepted

Existing multi-style speech synthesis methods require either style labels or large amounts of unlabeled training data, making data acquisition difficult. In this paper, we present an unsupervised multi-style speech synthesis method that can be trained with limited data. We leverage instance discrimi…

Cited by 0SourceScholar
2020

Flow-TTS: A Non-Autoregressive Network for Text to Speech Based on Flow

ICASSP 2020accepted

In this work, we propose Flow-TTS, a non-autoregressive end-to-end neural TTS model based on generative flow. Unlike other non-autoregressive models, Flow-TTS can achieve high-quality speech generation by using a single feed-forward network. To our knowledge, Flow-TTS is the first TTS model utilizin…

Cited by 0SourceScholar