Neural MOS Prediction for Synthesized Speech Using Multi-Task Learning with Spoofing Detection and Spoofing Type Classification

There is massive amount of news on financial events every day. In this paper, we present a unified model for detecting, classifying and summarizing financial events. This model exploits a multi-task learning approach, in which a pre-trained BERT model is used to encode the news articles, and the encoded information are shared by event type classification, detection and summarization tasks. For event summarization, we use a Transformer structure as the decoder. In addition to the input document encoded by BERT, the decoder also utilizes the predicted event type and cluster information, so that it can focus on the specific aspects of the event when generating summary. Our experiments show that our approach outperforms other methods.

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Similar floor plan retrieval featuring multi-task learning of layout type classification and room presence prediction

2018 IEEE International Conference on Consumer Electronics (ICCE) ◽

10.1109/icce.2018.8326163 ◽

2018 ◽

Cited By ~ 5

Author(s):

Yuki Takada ◽

Naoto Inoue ◽

Toshihiko Yamasaki ◽

Kiyoharu Aizawa

Keyword(s):

Floor Plan ◽

Task Learning ◽

Plan Retrieval ◽

Type Classification

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Weakly Supervised Multi-task Learning for Semantic Parsing

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/468 ◽

2019 ◽

Author(s):

Bo Shao ◽

Yeyun Gong ◽

Junwei Bao ◽

Jianshu Ji ◽

Guihong Cao ◽

...

Keyword(s):

State Of The Art ◽

Logical Form ◽

Learning Model ◽

Question Type ◽

Learning Method ◽

Semantic Parsing ◽

Task Learning ◽

Weakly Supervised ◽

Type Information ◽

Type Classification

Semantic parsing is a challenging and important task which aims to convert a natural language sentence to a logical form. Existing neural semantic parsing methods mainly use <question, logical form> (Q-L) pairs to train a sequence-to-sequence model. However, the amount of existing Q-L labeled data is limited and hard to obtain. We propose an effective method which substantially utilizes labeling information from other tasks to enhance the training of a semantic parser. We design a multi-task learning model to train question type classification, entity mention detection together with question semantic parsing using a shared encoder. We propose a weakly supervised learning method to enhance our multi-task learning model with paraphrase data, based on the idea that the paraphrased questions should have the same logical form and question type information. Finally, we integrate the weakly supervised multi-task learning method to an encoder-decoder framework. Experiments on a newly constructed dataset and ComplexWebQuestions show that our proposed method outperforms state-of-the-art methods which demonstrates the effectiveness and robustness of our method.

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Perception of Distorted "R" Sounds in the Synthesized Speech of Chlldren and Adults

Journal of Speech Language and Hearing Research ◽

10.1044/jshr.2604.516 ◽

1983 ◽

Vol 26 (4) ◽

pp. 516-524 ◽

Cited By ~ 6

Author(s):

Donald J. Sharf ◽

Ralph N. Ohde

Keyword(s):

Speech Language Pathology ◽

Graduate Level ◽

Point Scale ◽

Synthesized Speech ◽

Language Pathology ◽

High Degree

Adult and Child manifolds were generated by synthesizing 5 X 5 matrices of/Cej/ type utterances in which F2 and F3 frequencies were systematically varied. Manifold stimuli were presented to 11 graduate-level speech-language pathology students in two conditions: (a) a rating condition in which stimuli were rated on a 4-point scale between good /r/and good /w/; and (b) a labeling condition in which stimuli were labeled as "R," "W," "distorted R." or "N" (for none of the previous choices). It was found that (a) stimuli with low F2 and high F3 frequencies were rated 1.0nmdas;1.4; those with high F2 and low F3 frequencies were rated 3.6–4.0, and those with intermediate values were rated 1.5–3.5; (b) stimuli rated 1.0–1.4 were labeled as "W" and stimuli rated 3.6–4.0 were labeled as "R"; (c) none of the Child manifold stimuli were labeled as distorted "R" and one of the Adult manifold stimuli approached a level of identification that approached the percentage of identification for "R" and "W": and (d) rating and labeling tasks were performed with a high degree of reliability.

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