Search Results for author: Xiong Xiao

Found 24 papers, 6 papers with code

Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing

1 code implementation4 Feb 2024 Yinqiu Huang, Shuli Wang, Min Gao, Xue Wei, Changhao Li, Chuan Luo, Yinhua Zhu, Xiong Xiao, Yi Luo

ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment.

Marketing

NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription

no code implementations16 Jan 2024 Alon Vinnikov, Amir Ivry, Aviv Hurvitz, Igor Abramovski, Sharon Koubi, Ilya Gurvich, Shai Pe`er, Xiong Xiao, Benjamin Martinez Elizalde, Naoyuki Kanda, Xiaofei Wang, Shalev Shaer, Stav Yagev, Yossi Asher, Sunit Sivasankaran, Yifan Gong, Min Tang, Huaming Wang, Eyal Krupka

The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics.

Automatic Speech Recognition Benchmarking +4

A robust method for reliability updating with equality information using sequential adaptive importance sampling

no code implementations8 Mar 2023 Xiong Xiao, Zeyu Wang, Quanwang Li

Reliability updating refers to a problem that integrates Bayesian updating technique with structural reliability analysis and cannot be directly solved by structural reliability methods (SRMs) when it involves equality information.

Computational Efficiency

Speaker Change Detection for Transformer Transducer ASR

no code implementations16 Feb 2023 Jian Wu, Zhuo Chen, Min Hu, Xiong Xiao, Jinyu Li

Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word sequence into paragraphs at speaker change points.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings

1 code implementation30 Mar 2022 Naoyuki Kanda, Jian Wu, Yu Wu, Xiong Xiao, Zhong Meng, Xiaofei Wang, Yashesh Gaur, Zhuo Chen, Jinyu Li, Takuya Yoshioka

The proposed speaker embedding, named t-vector, is extracted synchronously with the t-SOT ASR model, enabling joint execution of speaker identification (SID) or speaker diarization (SD) with the multi-talker transcription with low latency.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Streaming Multi-Talker ASR with Token-Level Serialized Output Training

1 code implementation2 Feb 2022 Naoyuki Kanda, Jian Wu, Yu Wu, Xiong Xiao, Zhong Meng, Xiaofei Wang, Yashesh Gaur, Zhuo Chen, Jinyu Li, Takuya Yoshioka

This paper proposes a token-level serialized output training (t-SOT), a novel framework for streaming multi-talker automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR

no code implementations7 Oct 2021 Naoyuki Kanda, Xiong Xiao, Yashesh Gaur, Xiaofei Wang, Zhong Meng, Zhuo Chen, Takuya Yoshioka

Similar to the target-speaker voice activity detection (TS-VAD)-based diarization method, the E2E SA-ASR model is applied to estimate speech activity of each speaker while it has the advantages of (i) handling unlimited number of speakers, (ii) leveraging linguistic information for speaker diarization, and (iii) simultaneously generating speaker-attributed transcriptions.

Action Detection Activity Detection +6

Diarisation using location tracking with agglomerative clustering

no code implementations22 Sep 2021 Jeremy H. M. Wong, Igor Abramovski, Xiong Xiao, Yifan Gong

Previous works have shown that spatial location information can be complementary to speaker embeddings for a speaker diarisation task.

Clustering

A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio

no code implementations6 Jul 2021 Naoyuki Kanda, Xiong Xiao, Jian Wu, Tianyan Zhou, Yashesh Gaur, Xiaofei Wang, Zhong Meng, Zhuo Chen, Takuya Yoshioka

Our evaluation on the AMI meeting corpus reveals that after fine-tuning with a small real data, the joint system performs 8. 9--29. 9% better in accuracy compared to the best modular system while the modular system performs better before such fine-tuning.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Speaker attribution with voice profiles by graph-based semi-supervised learning

no code implementations6 Feb 2021 Jixuan Wang, Xiong Xiao, Jian Wu, Ranjani Ramamurthy, Frank Rudzicz, Michael Brudno

Speaker attribution is required in many real-world applications, such as meeting transcription, where speaker identity is assigned to each utterance according to speaker voice profiles.

Speaker Identification

Speaker diarization with session-level speaker embedding refinement using graph neural networks

no code implementations22 May 2020 Jixuan Wang, Xiong Xiao, Jian Wu, Ranjani Ramamurthy, Frank Rudzicz, Michael Brudno

Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a global loss defined on the training data, which could be sub-optimal for distinguishing speakers locally in a specific meeting session.

Clustering speaker-diarization +1

Continuous speech separation: dataset and analysis

1 code implementation30 Jan 2020 Zhuo Chen, Takuya Yoshioka, Liang Lu, Tianyan Zhou, Zhong Meng, Yi Luo, Jian Wu, Xiong Xiao, Jinyu Li

In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a \textit{continuous} audio stream that contains multiple utterances that are \emph{partially} overlapped by a varying degree.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

PyKaldi2: Yet another speech toolkit based on Kaldi and PyTorch

1 code implementation12 Jul 2019 Liang Lu, Xiong Xiao, Zhuo Chen, Yifan Gong

While similar toolkits are available built on top of the two, a key feature of PyKaldi2 is sequence training with criteria such as MMI, sMBR and MPE.

speech-recognition Speech Recognition

Low-Latency Speaker-Independent Continuous Speech Separation

no code implementations13 Apr 2019 Takuya Yoshioka, Zhuo Chen, Changliang Liu, Xiong Xiao, Hakan Erdogan, Dimitrios Dimitriadis

Speaker independent continuous speech separation (SI-CSS) is a task of converting a continuous audio stream, which may contain overlapping voices of unknown speakers, into a fixed number of continuous signals each of which contains no overlapping speech segment.

speech-recognition Speech Recognition +1

Recognizing Overlapped Speech in Meetings: A Multichannel Separation Approach Using Neural Networks

no code implementations8 Oct 2018 Takuya Yoshioka, Hakan Erdogan, Zhuo Chen, Xiong Xiao, Fil Alleva

The goal of this work is to develop a meeting transcription system that can recognize speech even when utterances of different speakers are overlapped.

speech-recognition Speech Recognition +1

Developing Far-Field Speaker System Via Teacher-Student Learning

no code implementations14 Apr 2018 Jinyu Li, Rui Zhao, Zhuo Chen, Changliang Liu, Xiong Xiao, Guoli Ye, Yifan Gong

In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system.

Keyword Spotting Model Compression +1

Spoofing detection under noisy conditions: a preliminary investigation and an initial database

no code implementations9 Feb 2016 Xiaohai Tian, Zhizheng Wu, Xiong Xiao, Eng Siong Chng, Haizhou Li

To simulate the real-life scenarios, we perform a preliminary investigation of spoofing detection under additive noisy conditions, and also describe an initial database for this task.

Speaker Verification

Fantastic 4 system for NIST 2015 Language Recognition Evaluation

no code implementations5 Feb 2016 Kong Aik Lee, Ville Hautamäki, Anthony Larcher, Wei Rao, Hanwu Sun, Trung Hieu Nguyen, Guangsen Wang, Aleksandr Sizov, Ivan Kukanov, Amir Poorjam, Trung Ngo Trong, Xiong Xiao, Cheng-Lin Xu, Hai-Hua Xu, Bin Ma, Haizhou Li, Sylvain Meignier

This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).

regression

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