46 citations · 87 across the 9 of their papers we have counts for
9 papers
T-Mimi: A Transformer-based Mimi Decoder for Real-Time On-Phone TTS
Haibin Wu, Bach Viet Do, Naveen Suda +10
Neural audio codecs provide promising acoustic features for speech synthesis, with representative streaming codecs like Mimi providing high-quality acoustic features for real-time…
Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems
Xing Lei, Longjun Liu, Zhiheng Zhou +2
In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can…
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models
Yuan Shangguan, Haichuan Yang, Danni Li +11
Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model's hyperparameters…
SCA: Streaming Cross-attention Alignment for Echo Cancellation
Yang Liu, Yangyang Shi, Yun Li +3
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art…
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition
Di Wu, Binbin Zhang, Chao Yang +4
The unified streaming and non-streaming two-pass (U2) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy, real-time fact…
WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
Zhuoyuan Yao, Di Wu, Xiong Wang +7
In this paper, we propose an open source, production first, and production ready speech recognition toolkit called WeNet in which a new two-pass approach is implemented to unify st…
Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition
Binbin Zhang, Di Wu, Zhuoyuan Yao +7
In this paper, we present a novel two-pass approach to unify streaming and non-streaming end-to-end (E2E) speech recognition in a single model. Our model adopts the hybrid CTC/atte…
Knowledge Distillation For Recurrent Neural Network Language Modeling With Trust Regularization
Yangyang Shi, Mei-Yuh Hwang, Xin Lei +1
Recurrent Neural Networks (RNNs) have dominated language modeling because of their superior performance over traditional N-gram based models. In many applications, a large Recurren…
End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model
Yangyang Shi, Mei-Yuh Hwang, Xin Lei
Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based end-to-end models are widely used in speech recognition due to its simplicity in training and efficien…