Exploiting deep neural networks for detection-based speech recognition

By Sabato Marco Siniscalchi, Dong Yu, Li Deng, Chin-Hui Lee. 2013

128 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. Jinyu Li
4. Yonatan Belinkov
5. James R. Glass
6. Chin-Hui Lee
7. Khe Chai Sim
8. Xiaodong He

In Signal Processing

1. Dong Yu
2. Li Deng
3. Jinyu Li
4. Khe Chai Sim
5. Chin-Hui Lee
6. Sabato Marco Siniscalchi
7. Yifan Gong
8. Xiaodong He

Citations in the corpus

• Deep Learning: Methods and Applications (2014, methodology): “Finally, the learning architecture developed for bottom-up, detection-based speech recognition proposed in [214] and developed further since 2004, notably in [330, 332, 427] using the DBN–DNN technique, can also be categorized in the discriminative or supervisedlearning deep architecture category.”
• Encoding of phonology in a recurrent neural model of grounded speech (2017, background): “Siniscalchi et al. (2013) show that a multilayer perceptron can successfully classify phonological features and contribute to the accuracy of a downstream ASR system.”
• Context-dependent modelling of deep neural network using logistic regression (2013, methodology): “The proposed work is also different from the detector-based automatic speech recognition approach [14, 15, 16], where NN [14] or DNN [15] with binary outputs are used to detect the articulator attributes in speech and a merger is used to combine various attributes to predict the state output posteri”
• Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “Examples include deep-structured CRF, tandem-MLP architecture [94], [258], deep convex or stacking network [248] and its tensor version [242], [243], [259], and detection-based ASR architecture [235], [236], [260].”
• Optimization Algorithms and Applications for Speech and Language Processing (2013, methodology): “Thus, by applying the Taylor series argument mentioned above, we can show that for all sufficiently large—we are guaranteed to see ascent in .”
• Regression-Based Context-Dependent Modeling of Deep Neural Networks for Speech Recognition (2014, methodology): “The proposed work is also different from the detector-based automatic speech recognition approach [22], [23], [24], where NN [22] or DNN [23] with binary outputs are used to detect the articulator attributes in speech and a merger is used to combine various attributes to predict the state output pos”
• Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems (2013, background): “, [7], [42], [14], [43]) due to the great success of deep neural networks—MLPs with many hidden layers that adopts the pre-training approach proposed for generative deep belief nets [44]—in many speech applications (e.”