合 作 者
期刊 & 会议
年  份
Kazumasa Yamamoto 的论文(48) 排序方式:
Development and Evaluation of Spoken Dialog Systems with One or Two Agents through Two Domains  
Temporal Logic in Specification  2013
0次引用 0 0
Development and evaluation of spoken dialog systems with one or two agents  
Annual Conference of the International Speech Communication Association  2013
0次引用 0 0
A robust/fast spoken term detection method based on a syllable n-gram index with a distance metric  
Abstract For spoken document retrieval, it is crucial to consider out-of-vocabulary (OOV) and the mis-recognition of spoken words. Consequently, sub-word unit based recognition and retrieval methods h......
Speech Communication  2013
0次引用 0 0
Hidden Conditional Neural Fields for Continuous Phoneme Speech Recognition  
Ieice Transactions  2012
0次引用 0 0
Improving the Readability of ASR Results for Lectures Using Multiple Hypotheses and Sentence-Level Knowledge  
This paper presents a novel method for improving the readability of automatic speech recognition (ASR) results for classroom lectures. Because speech in a classroom is spontaneous and contains many il......
Ieice Transactions  2012
0次引用 0 0
Efficient out-of-vocabulary term detection by n-gram array indices with distance from a syllable lattice  
For spoken document retrieval, it is very important to con sider Out-of-Vocabulary (OOV) and mis-recognition of spoken words. Therefore, sub-word unit based recognition and retrieval methods have been......
International Conference on Acoustics, Speech, and Signal Processing  2011
0次引用 0 0
Hidden Boosted MMI and Hierarchical State Posterior Feature for Automatic Speech Recognition Based on Hidden Conditional Neural Fields  
Annual Conference of the International Speech Communication Association  2011
0次引用 0 0
Speech Recognition in Mixed Sound of Speech and Music Based on Vector Quantization and Non-Negative Matrix Factorization  
Annual Conference of the International Speech Communication Association  2011
0次引用 0 0
Automatic speech recognition using Hidden Conditional Neural Fields  
Hidden Conditional Random Fields(HCRF) is a very promising approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted features, it cannot consid......
International Conference on Acoustics, Speech, and Signal Processing  2011
3次引用 0 0
Speech recognition using long-term phase information  
Abstract Current speech recognition systems use mainly amplitude spectrum-based features such as MFFC for acoustic feature parameters, while discarding phase spectral information. The results of perce......
Annual Conference of the International Speech Communication Association  2010
0次引用 0 0

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