An Integrative and Discriminative Technique for Spoken Utterance Classification

By Sibel Yaman, Li Deng, Dong Yu, Ye-Yi Wang, A. Acero. 2008

65 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Li Deng
2. Dong Yu
3. Jianfeng Gao
4. Xiaodong He
5. Yoshua Bengio
6. Kaisheng Yao
7. Mohit Bansal
8. Geoffry Zweig

In Signal Processing

1. Li Deng
2. Dong Yu
3. Yoshua Bengio
4. Xiaodong He
5. Jinyu Li
6. Xiao Li
7. Gökhan Tür
8. Kaisheng Yao

Citations in the corpus

• A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “…hybrid deep learning approach can be applied to not only speech translation but also all speech-centric and possibly other information-processing tasks such as speech information retrieval, speech understanding, cross-lingual speech/text understanding and retrieval, etc. (e.g., [11, 109, 149–153]).”
• Neural Approaches to Conversational AI (2018, unknown): “Among them, the first two tasks are often framed as a classification problem, which infers the domain or intent (from a predefined set of candidates) based on the current user utterance (Schapire and Singer, 2000; Yaman et al., 2008; Sarikaya et al., 2014).”
• Machine Learning Paradigms for Speech Recognition: An Overview (2013, methodology): “Using this principle, a new MCE-based learning algorithm is developed in [106] with success for a speech understanding task which embeds ASR as a sub-component, where the parameters of a log linear model is learned via a generalized MCE criterion.”
• Speech-Centric Information Processing: An Optimization-Oriented Approach (2013, methodology): “Most recently, deep learning technique has been successfully applied to intent determination, as reported by [81], [103].”
• Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding (2013, unknown): “with the intent determination task [23][27].”
• Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding (2015, background): “[4], [30], [62], [63].”
• Towards deeper understanding: Deep convex networks for semantic utterance classification (2012, methodology): “Therefore, joint optimization of the ASR module and the SUC module, in similar ways to the schemes developed in [5, 21], is desirable.”