4 citations · 9 across the 12 of their papers we have counts for
12 papers
Latent Softmax for Data-Efficient Phoneme-Based Multilingual ASR Across Tonal and Non-Tonal Languages
Saierdaer Yusuyin, Nanling Jiang, Hao Huang +1
Phoneme-based multilingual automatic speech recognition (ASR) can share acoustic evidence across languages more directly than language-specific subword modeling. When tonal and non…
CTC-TTS: LLM-based dual-streaming text-to-speech with CTC alignment
Hanwen Liu, Saierdaer Yusuyin, Hao Huang +1
Large-language-model (LLM)-based text-to-speech (TTS) systems can generate natural speech, but most are not designed for low-latency dual-streaming synthesis. High-quality dual-str…
Phoneme-based speech recognition driven by large language models and sampling marginalization
Te Ma, Nanjie Li, Hao Huang +1
Recently, the Large Language Model-based Phoneme-to-Grapheme (LLM-P2G) method has shown excellent performance in speech recognition tasks and has become a feasible direction to rep…
Pronunciation-Lexicon Free Training for Phoneme-based Crosslingual ASR via Joint Stochastic Approximation
Saierdaer Yusuyin, Te Ma, Hao Huang +1
Recently, pre-trained models with phonetic supervision have demonstrated their advantages for crosslingual speech recognition in data efficiency and information sharing across lang…
LLM-based phoneme-to-grapheme for phoneme-based speech recognition
Te Ma, Min Bi, Saierdaer Yusuyin +2
In automatic speech recognition (ASR), phoneme-based multilingual pre-training and crosslingual fine-tuning is attractive for its high data efficiency and competitive results compa…
Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform
Xiangzhu Kong, Huang Hao, Zhijian Ou
This paper presents SHTNet, a lightweight spherical harmonic transform (SHT) based framework, which is designed to address cross-array generalization challenges in multi-channel au…
Low-Resourced Speech Recognition for Iu Mien Language via Weakly-Supervised Phoneme-based Multilingual Pre-training
Lukuan Dong, Donghong Qin, Fengbo Bai +4
The mainstream automatic speech recognition (ASR) technology usually requires hundreds to thousands of hours of annotated speech data. Three approaches to low-resourced ASR are pho…
CUSIDE-T: Chunking, Simulating Future and Decoding for Transducer based Streaming ASR
Wenbo Zhao, Ziwei Li, Chuan Yu +1
Streaming automatic speech recognition (ASR) is very important for many real-world ASR applications. However, a notable challenge for streaming ASR systems lies in balancing operat…
CUSIDE-array: A Streaming Multi-Channel End-to-End Speech Recognition System with Realistic Evaluations
Xiangzhu Kong, Tianqi Ning, Hao Huang +1
Recently multi-channel end-to-end (ME2E) ASR systems have emerged. While streaming single-channel end-to-end ASR has been extensively studied, streaming ME2E ASR is limited in expl…
Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision
Saierdaer Yusuyin, Te Ma, Hao Huang +2
There exist three approaches for multilingual and crosslingual automatic speech recognition (MCL-ASR) - supervised pretraining with phonetic or graphemic transcription, and self-su…
Energy-Based Models with Applications to Speech and Language Processing
Zhijian Ou
Energy-Based Models (EBMs) are an important class of probabilistic models, also known as random fields and undirected graphical models. EBMs are un-normalized and thus radically di…
Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning
Wenyu Zhu, Zhiyao Feng, Zihan Zhang +4
Recovering binary programs' call graphs is crucial for inter-procedural analysis tasks and applications based on them.transfer One of the core challenges is recognizing targets of…