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PROMPTTTS 2: DESCRIBING AND GENERATING VOICES WITH TEXT PROMPT 12

PROMPTTTS 2: DESCRIBING AND GENERATING VOICES WITH TEXT PROM...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Leng, Yichong Guo, Zhifang Shen, Kai Ju, Zeqian Tan, Xu Liu, Yanqing Liu, Yufei Yang, Dongchao Zhang, Leying Song, Kaitao He, Lei Li, Xiang-Yang Zhao, Sheng Qin, Tao Bian, Jiang MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Microsoft China

Speech conveys more information than text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two main challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompts for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice variability) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech language understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompts based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality text prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Large datasets

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Online travel destination recommendation with efficient variable memory Markov model

Online travel destination recommendation with efficient vari...

引用

2013 IEEE International Conference on multimedia and Expo Workshops, ICMEW 2013

作者： Kai, Jiang Yu, Nenghai Li, Weihai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei China

ISBN: (纸本)9781479916047

Online travel destination recommendation is to keep track of a user's current traveling history to recommend next destination in real time while the user is on the travel. This paper presents an efficient variable memory Markov model based method to provide such recommendation. The proposed method utilizes the large quantity of geotags from photo sharing website and combines travel pattern, location's popularity and distance factors to generate real time recommendation. Experiments on Panoramio data set demonstrate the effectiveness of this method. © 2013 IEEE.

关键词： Social networking (online)

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Online travel destination recommendation with efficient variable memory Markov model

Online travel destination recommendation with efficient vari...

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IEEE International Conference on multimedia and Expo Workshops (ICMEW)

作者： Kai Jiang Nenghai Yu Weihai Li MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei China

ISBN: (纸本)9781479916030

关键词： Markov processes Abstracts

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A new graph constructor for semi-supervised discriminant analysis via group sparsity

A new graph constructor for semi-supervised discriminant ana...

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International Conference on Image and Graphics

作者： Gao, Haoyuan Zhuang, Liansheng Yu, Nenghai MOE-MS Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei 230026 China

ISBN: (纸本)9780769545417

Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly labeled data. This paper studies the Semi-supervised Discriminant Analysis (SDA) algorithm, which aims at dimensionality reduction utilizing both limited labeled data and abundant unlabeled data. Different from other relative work, we pay our attention to graph construction, which plays a key role in graph based SSL methods. Inspired by the advances of compressive sensing, we propose a novel graph construction method via group sparsity,which means to constrain the reconstruct data to be sparse for each sample, and constrain the representation in each class to be quite similar. Experimental results show that our method can significantly improve the performance of SDA, and outperform state-of-the-art methods. © 2011 IEEE.

关键词： Supervised learning

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Semi-supervised classification via low rank graph

Semi-supervised classification via low rank graph

引用

International Conference on Image and Graphics

作者： Zhuang, Liansheng Gao, Haoyuan Huang, Jingjing Yu, Nenghai MOE-MS Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei 230026 China

ISBN: (纸本)9780769545417

Graph plays a very important role in graph based semi-supervised learning (SSL) methods. However, most current graph construction methods emphasize on local properties of the graph. In this paper, inspired by the advances of compressive sensing, we present a novel method to construct a so-called low-rank graph (LR-graph) for graph based SSL methods. Assuming that the graph is sparse and low rank, our proposed method uses both the local property and the global property of the graph, and thus is better at capturing the global structure of all data. Compared with current graphs, LR-graph is more informative and discriminative, and robust to outliers. Experiments on generic object recognition show that LR-graph achieves state-of-the-art performance for graph based SSL methods. © 2011 IEEE.

关键词： Supervised learning

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Tracking based on SURF and superpixel

Tracking based on SURF and superpixel

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International Conference on Image and Graphics

作者： Liu, Yu Zhou, Wei Yin, Huagang Yu, Nenghai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei China

ISBN: (纸本)9780769545417

In this paper we present a novel algorithm for object tracking in video sequence based on SURF key-point and superpixel. SURF key-point is very effective for object matching between two images and we can use it to locate object by and large. But only in this way it can't guarantee the success of object tracking because many available key-points are un-matched and can't be used to locate the object. In order to solve this problem we take the advantage of superpixel to obtain more available key-points based on its property that all points in the same superpixel region belong to the same object. At last we construct a weight map for each frame to refine the location of target *** experiments demonstrate that our work is robust for tracking, especially dealing with occlusions and object transformations. © 2011 IEEE.

关键词： Superpixels

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ContextRank: Personalized tourism recommendation by exploiting context information of geotaggedweb photos

ContextRank: Personalized tourism recommendation by exploiti...

引用

International Conference on Image and Graphics

作者： Jiang, Kai Wang, Peng Yu, Nenghai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei China

ISBN: (纸本)9780769545417

In this paper, we propose a method: ContextRank, which utilizes the vast quantity of geotagged photos in photo sharing website to recommend travel locations. To enhance the personalized recommendation performance, our method exploits different context information of photos, such as textual tags, geotags, visual information, and user similarity. Context-Rank first detects landmarks from photos' GPS locations, and estimates the popularity of each landmark. Within each landmark, representative photos and tags are extracted. Furthermore, ContextRank calculates the user similarity based on users' travel history. When a user's geotagged photos are given, the landmark popularity, representative photos and tags, and the user similarity are used to predict the user preference of a landmark from different aspects. Finally a learning to rank algorithm is introduced to combine different preference predictions to give the final recommendation. Experiments performed on a dataset collected from Panoramio show that the ContextRank can obtain a better result than the state-of-the-art method. © 2011 IEEE.

关键词： Semantics

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An automatic matching algorithm based on SIFT descriptors for remote sensing ship image

An automatic matching algorithm based on SIFT descriptors fo...

引用

International Conference on Image and Graphics

作者： You, Xin Yu, Nenghai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China HeFei Anhui China

ISBN: (纸本)9780769545417

SIFT judge the true or false targets according to the density of matching feature points. But for different images, the numbers of matching feature points are different, the density is different. So SIFT can't judge the real targets automatically, requiring human-computer interaction. This paper proposes an algorithm based on SIFT realizing the automatic judgment of real targets. Firstly, do the image segmentation. Secondly, match the feature points in every area. At last, do frequency domain analysis for each matching feature vectors. The experimental results show that our algorithm solves the problem that SIFT judges the real target needing human-computer interaction. and also, our algorithm can determine the location and number of real targets. © 2011 IEEE.

关键词： Frequency domain analysis

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Edge saliency map detection with texture suppression

Edge saliency map detection with texture suppression

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International Conference on Image and Graphics

作者： Ao, Huanhuan Yu, Nenghai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9780769545417

Edge is a basic feature in the field of computing vision. So to find edge saliency map is an indispensable operation for many applications on image processing. In this paper we present a fast algorithm to find edge saliency map for a natural image. The approach integrates three basic edge features: edge gradient value, edge segment length and edge density, and it works well to detect salient region boundaries and to suppress ill edges from texture. An edge saliency map can be used to image segmentation and boundaries detection. Experimental results demonstrate that our algorithm outperforms other edge saliency detection methods. Finally, our algorithm is applied on salient objects segmentation, compared with several stateof- the-art salient region detection methods and the results show our work is valuable. © 2011 IEEE.

关键词： Image segmentation

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A sea-land segmentation scheme based on statistical model of sea

A sea-land segmentation scheme based on statistical model of...

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4th International Congress on Image and Signal Processing, CISP 2011

作者： You, Xin Li, Weihai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781424493067

Sea-land segmentation is a key step for target detection. Due to the complex texture and uneven gray value of the land in optical remote sensing image, traditional sea-land segmentation algorithms often recognize land as sea incorrectly. A new segmentation scheme is presented in this paper to solve this problem. This scheme determines the threshold according to the adaptively established statistical model of the sea area, and removes the incorrectly classified land according to the difference of the variance in the statistical model between land and sea. Experimental results show our segmentation scheme has small computation complexity, and it has better performance and higher robustness compared to the traditional algorithms. © 2011 IEEE.

关键词： Optical remote sensing

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