2 citations · 9 across the 13 of their papers we have counts for
14 papers
ATTEST: Evidence-Grounded Reinforcement Learning for Long-Audio Temporal Understanding
Ashutosh Chaubey, Jinxi Guo, Minh Tran +2
Temporal grounding is fundamental to audio understanding, yet large audio language models (LALMs) remain weak at it on long audio, where hallucinations compound and annotated data…
Towards scalable efficient on-device ASR with transfer learning
Laxmi Pandey, Ke Li, Jinxi Guo +4
Multilingual pretraining for transfer learning significantly boosts the robustness of low-resource monolingual ASR models. This study systematically investigates three main aspects…
Effective internal language model training and fusion for factorized transducer model
Jinxi Guo, Niko Moritz, Yingyi Ma +6
The internal language model (ILM) of the neural transducer has been widely studied. In most prior work, it is mainly used for estimating the ILM score and is subsequently subtracte…
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model
Jiamin Xie, Ke Li, Jinxi Guo +7
Neural network pruning offers an effective method for compressing a multilingual automatic speech recognition (ASR) model with minimal performance loss. However, it entails several…
Prompting Large Language Models with Speech Recognition Abilities
Yassir Fathullah, Chunyang Wu, Egor Lakomkin +9
Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. I…
Improving Fast-slow Encoder based Transducer with Streaming Deliberation
Ke Li, Jay Mahadeokar, Jinxi Guo +5
This paper introduces a fast-slow encoder based transducer with streaming deliberation for end-to-end automatic speech recognition. We aim to improve the recognition accuracy of th…
Biased Self-supervised learning for ASR
Florian L. Kreyssig, Yangyang Shi, Jinxi Guo +3
Self-supervised learning via masked prediction pre-training (MPPT) has shown impressive performance on a range of speech-processing tasks. This paper proposes a method to bias self…
VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end Long-form Speech Recognition
Jinhan Wang, Xiaosu Tong, Jinxi Guo +2
While end-to-end models have shown great success on the Automatic Speech Recognition task, performance degrades severely when target sentences are long-form. The previous proposed…
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Hu Hu, Xuesong Yang, Zeynab Raeesy +6
Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address this problem by building an accent-robust RNN-T system with domain adversarial training (DA…
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification
Amber Afshan, Jinxi Guo, Soo Jin Park +3
The effects of speaking-style variability on automatic speaker verification were investigated using the UCLA Speaker Variability database which comprises multiple speaking styles p…
Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition
Jinxi Guo, Gautam Tiwari, Jasha Droppo +4
In this work, we propose a novel and efficient minimum word error rate (MWER) training method for RNN-Transducer (RNN-T). Unlike previous work on this topic, which performs on-the-…
Singing voice conversion with non-parallel data
Xin Chen, Wei Chu, Jinxi Guo +1
Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voic…
A spelling correction model for end-to-end speech recognition
Jinxi Guo, Tara N. Sainath, Ron J. Weiss
Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and r…
Deep neural network based i-vector mapping for speaker verification using short utterances
Jinxi Guo, Ning Xu, Kailun Qian +4
Text-independent speaker recognition using short utterances is a highly challenging task due to the large variation and content mismatch between short utterances. I-vector based sy…