2013 IEEE Workshop on Automatic Speech Recognition and Understanding最新文献_第3页

Convolutional neural network based triangular CRF for joint intent detection and slot filling 基于卷积神经网络的三角CRF联合意图检测与缝隙填充

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707709

Puyang Xu, R. Sarikaya

引用次数: 316

Mixture of mixture n-gram language models 混合n-gram语言模型的混合

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707701

H. Sak, Cyril Allauzen, Kaisuke Nakajima, F. Beaufays

{"title":"Mixture of mixture n-gram language models","authors":"H. Sak, Cyril Allauzen, Kaisuke Nakajima, F. Beaufays","doi":"10.1109/ASRU.2013.6707701","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707701","url":null,"abstract":"This paper presents a language model adaptation technique to build a single static language model from a set of language models each trained on a separate text corpus while aiming to maximize the likelihood of an adaptation data set given as a development set of sentences. The proposed model can be considered as a mixture of mixture language models. The mixture model at the top level is a sentence-level mixture model where each sentence is assumed to be drawn from one of a discrete set of topic or task clusters. After selecting a cluster, each n-gram is assumed to be drawn from one of the given n-gram language models. We estimate cluster mixture weights and n-gram language model mixture weights for each cluster using the expectation-maximization (EM) algorithm to seek the parameter estimates maximizing the likelihood of the development sentences. This mixture of mixture models can be represented efficiently as a static n-gram language model using the previously proposed Bayesian language model interpolation technique. We show a significant improvement with this technique (both perplexity and WER) compared to the standard one level interpolation scheme.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129989742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Towards unsupervised semantic retrieval of spoken content with query expansion based on automatically discovered acoustic patterns 基于自动发现声学模式的查询扩展的口语内容无监督语义检索

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707729

Yun-Chiao Li, Hung-yi Lee, Cheng-Tao Chung, Chun-an Chan, Lin-Shan Lee

{"title":"Towards unsupervised semantic retrieval of spoken content with query expansion based on automatically discovered acoustic patterns","authors":"Yun-Chiao Li, Hung-yi Lee, Cheng-Tao Chung, Chun-an Chan, Lin-Shan Lee","doi":"10.1109/ASRU.2013.6707729","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707729","url":null,"abstract":"This paper presents an initial effort to retrieve semantically related spoken content in a completely unsupervised way. Unsupervised approaches of spoken content retrieval is attractive because the need for annotated data reasonably matched to the spoken content for training acoustic and language models can be bypassed. However, almost all such unsupervised approaches focus on spoken term detection, or returning the spoken segments containing the query, using either template matching techniques such as dynamic time warping (DTW) or model-based approaches. However, users usually prefer to retrieve all objects semantically related to the query, but not necessarily including the query terms. This paper proposes a different approach. We transcribe the spoken segments in the archive to be retrieved through into sequences of acoustic patterns automatically discovered in an unsupervised method. For an input query in spoken form, the top-N spoken segments from the archive obtained with the first-pass retrieval with DTW are taken as pseudo-relevant. The acoustic patterns frequently occurring in these segments are therefore considered as query-related and used for query expansion. Preliminary experiments performed on Mandarin broadcast news offered very encouraging results.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133765456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Automatic sentiment extraction from YouTube videos 从YouTube视频中自动提取情感

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707736

L. Kaushik, A. Sangwan, J. Hansen

{"title":"Automatic sentiment extraction from YouTube videos","authors":"L. Kaushik, A. Sangwan, J. Hansen","doi":"10.1109/ASRU.2013.6707736","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707736","url":null,"abstract":"Extracting speaker sentiment from natural audio streams such as YouTube is challenging. A number of factors contribute to the task difficulty, namely, Automatic Speech Recognition (ASR) of spontaneous speech, unknown background environments, variable source and channel characteristics, accents, diverse topics, etc. In this study, we build upon our previous work [5], where we had proposed a system for detecting sentiment in YouTube videos. Particularly, we propose several enhancements including (i) better text-based sentiment model due to training on larger and more diverse dataset, (ii) an iterative scheme to reduce sentiment model complexity with minimal impact on performance accuracy, (iii) better speech recognition due to superior acoustic modeling and focused (domain dependent) vocabulary/language models, and (iv) a larger evaluation dataset. Collectively, our enhancements provide an absolute 10% improvement over our previous system in terms of sentiment detection accuracy. Additionally, we also present analysis that helps understand the impact of WER (word error rate) on sentiment detection accuracy. Finally, we investigate the relative importance of different Parts-of-Speech (POS) tag features towards sentiment detection. Our analysis reveals the practicality of this technology and also provides several potential directions for future work.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116639731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Dysfluent speech detection by image forensics techniques 基于图像取证技术的语音障碍检测

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707712

Juraj Pálfy, Sakhia Darjaa, Jiri Pospíchal

引用次数: 1

Hybrid speech recognition with Deep Bidirectional LSTM 基于深度双向LSTM的混合语音识别

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707742

Alex Graves, N. Jaitly, Abdel-rahman Mohamed

引用次数: 1517

Barge-in effects in Bayesian dialogue act recognition and simulation 贝叶斯对话行为识别与模拟中的碰撞效应

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707713

H. Cuayáhuitl, Nina Dethlefs, H. Hastie, Oliver Lemon

引用次数: 6

Vector Taylor series based HMM adaptation for generalized cepstrum in noisy environment 噪声环境下基于矢量泰勒级数的广义倒谱HMM自适应

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707727

Soonho Baek, Hong-Goo Kang

引用次数: 1

Improving robustness of deep neural networks via spectral masking for automatic speech recognition 基于频谱掩蔽的深度神经网络鲁棒性研究

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707743

Bo Li, K. Sim

引用次数: 40

Impact of deep MLP architecture on different acoustic modeling techniques for under-resourced speech recognition 深度MLP架构对资源不足语音识别中不同声学建模技术的影响

2013 IEEE Workshop on Automatic Speech Recognition and Understanding Pub Date : 2013-12-01 DOI: 10.1109/ASRU.2013.6707752

David Imseng, P. Motlícek, Philip N. Garner, H. Bourlard

引用次数: 28