Using neural network front-ends on far field multiple microphones based speech recognition

By Yulan Liu, Pengyuan Zhang, Thomas Hain. 2014

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Speech & Audio, Signal Processing

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Citations in the corpus

The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, background): “Although considerable progresses were made at multi-microphone frontend processing level in order to feed ASR with an enhanced speech input [4, 5, 6, 7, 8], the performance loss observed from close-talking to distant-speech remains quite critical, even when the most advanced DNN-based backend framew”
Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, background): “Concerning the latter field, the research community is currently experiencing a revolution due to the introduction of Deep Neural Networks (DNNs) [13], which have consistently outperformed previous Gaussian Mixture Models (GMMs) for both close [14] and distant-talking scenarios [15, 16, 17, 18, 19].”
Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models (2014, methodology): “Other examples of auxiliary features include the use of speaker-specific bottleneck features obtained from a speaker separation DNN used in a distant speech recognition task [26] and the use of out-of-domain tandem features [14].”
Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, background): “Despite the recent substantial progress in various related fields, including spatial filtering [1, 2], microphone selection [3], source separation [4], speech dereverberation [5], speaker localization [6], acoustic event detection [7] as well as acoustic modeling [8, 9, 10, 11, 12], DSR still exhibi”
Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments (2014, background): “In [10] and [11], feature vectors from multiple microphones are concatenated to form the input of a DNN-based acoustic model, however, no spatial phase information is exploited.”
Factorized Hidden Layer Adaptation for Deep Neural Network Based Acoustic Modeling (2016, background): “There have been a number of attempts to combine GMM and DNN to leverage on the reliable adaptation performance of the former and high quality classification performance of the latter.”
Batch-normalized joint training for DNN-based distant speech recognition (2016, background): “Nevertheless, the recent success of deep learning has not only largely contributed to the substantial improvement of the speech recognition part of a DSR system [19, 20, 21, 22, 23, 24, 25], but has also enabled the de-”