Error Approximation and Minimum Phone Error Acoustic Model Estimation

By Matthew Gibson, Thomas Hain. 2010

17 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. Xiaodong He
4. Jianfeng Gao
5. Yoshua Bengio
6. Xiao Li
7. Georg Heigold
8. Haizhou Li

In Signal Processing

1. Li Deng
2. Dong Yu
3. Xiaodong He
4. Xiao Li
5. Haizhou Li
6. Georg Heigold
7. Jinyu Li
8. Dimitri Kanevsky

Citations in the corpus

• Deep Learning: Methods and Applications (2014, background): “Many of the discriminative techniques for supervised learning in signal and information processing are shallow architectures such as HMMs [52, 127, 147, 186, 188, 290, 394, 418] and conditional random fields (CRFs) [151, 155, 281, 400, 429, 446].”
• A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “Many of the discriminative techniques in signal and information processing apply to shallow architectures such as HMMs (e.g., [87–94]) or CRFs (e.g., [95–100]).”
• Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “They include: 1)MMI [101], [121]; 2)MCE, which can be interpreted as minimal sentence error rate [79] or approximate minimal phone error rate [122]; 3) MPE or minimal phone error [73], [123]; and 4) MWE or minimal word error.”
• Optimization Algorithms and Applications for Speech and Language Processing (2013, methodology): “…approach is to use an approximation to the steepest descent direction , and define (9) In unconstrained optimization, this choice of will yield a direction of ascent for provided that (10) In other words, for all sufficiently small choices of in (4), we will identify a new iterate such that .”
• An Overview of Deep-Structured Learning for Information Processing (2011, background): “Many of the discriminative techniques in signal and information processing apply to shallow architectures such as HMMs (e.g., He et al., 2008; Jiang and Li, 2010; Xiao and Deng, 2010; Gibson and Hain, 2010) or CRFs (e.g., Yang and Furui, 2009; Yu, Li, and Deng, 2010; Hifny and Renals, 2009).”