Cocaine Noodles: Exploiting the Gap between Human and Machine Speech Recognition∗ Tavish Vaidya Georgetown University Yuankai Zhang

Source URL: security.cs.georgetown.edu

• Computational linguistics
• Speech recognition
• Mel-frequency cepstrum
• Speech synthesis
• Speech processing
• Deep learning
• Perception
• Acoustic model

Analysis of CNN-based Speech Recognition System using Raw Speech as Input Dimitri Palaz1,2 , Mathew Magimai.-Doss1 , Ronan Collobert3,1 Idiap Research Institute, Martigny, Switzerland Ecole Polytechnique F´ed´erale de

Source URL: ronan.collobert.com

• Artificial neural networks
• Computational neuroscience
• Computational linguistics
• Applied mathematics
• Artificial intelligence
• Mathematics
• Signal processing
• Deep learning
• Convolutional neural network
• Speech recognition
• Acoustic model
• Mel-frequency cepstrum

FEATURE COMPENSATION EMPLOYING ONLINE GMM ADAPTATION FOR SPEECH RECOGNITION IN UNKNOWN SEVERELY ADVERSE ENVIRONMENTS Wooil Kim and John H. L. Hansen Center for Robust Speech Systems (CRSS), Erik Jonsson School of Enginee

Source URL: www.utdallas.edu

• Linguistics
• Automatic identification and data capture
• Humancomputer interaction
• Speech recognition
• Human communication
• Computational linguistics
• Computer accessibility
• FMLLR
• Speech enhancement
• Cepstrum
• Speech corpus

Multi Channel Speech Dereverberation Using LP Residual Cepstrum in an Adaptive Beamforming Framework Karan Nathwani∗ , Shubham Khunteta∗ , Piyush Nathwani† and Rajesh M Hegde∗ Indian Institute of Technology Kanpu

Source URL: home.iitk.ac.in

Towards minimum perceptual error training for DNN-based speech synthesis Cassia Valentini-Botinhao, Zhizheng Wu, Simon King The Centre for Speech Technology Research, University of Edinburgh, United Kingdom cvbotinh@inf.

Source URL: www.cstr.ed.ac.uk

• Computational linguistics
• Signal processing
• Cepstrum
• Frequency domain analysis
• Speech synthesis
• Acoustic model
• Cepstral
• Electrical engineering
• Humancomputer interaction
• Computational science
• Speech recognition

ASJ Beginners Seminar 2013@Tokyo Univ. of Tech., Hachioji 「音響学入門ペディア」作成に向けて Q4: ケプストラムって要するに何ですか? What is cepstrum?

Source URL: abcpedia.acoustics.jp

PAPERS In Search of a Perceptual Metric for Timbre: Dissimilarity Judgments among Synthetic Sounds with MFCC-Derived Spectral Envelopes HIROKO TERASAWA,1,2 AES Member , JONATHAN BERGER3, AND SHOJI MAKINO1

Source URL: www.slis.tsukuba.ac.jp

• Image processing
• Vision
• Signal processing
• Mel-frequency cepstrum
• Timbre
• Spectral envelope
• Cepstrum
• Psychoacoustics
• Color vision
• Music
• Mind
• Sound

Audio Analysis using the Discrete Wavelet Transform George Tzanetakis, Georg Essl, Perry Cook* Computer Science Department *also Music Department Princeton 35 Olden Street, Princeton NJ 08544

Source URL: soundlab.cs.princeton.edu

• Numerical analysis
• Signal processing
• Functional analysis
• Wavelet
• Fourier analysis
• Discrete wavelet transform
• Short-time Fourier transform
• Mel-frequency cepstrum
• Wavelet packet decomposition
• Mathematical analysis
• Wavelets
• Mathematics

PDF Document

Source URL: soundsofthecity.arts.gla.ac.uk

• Vowels
• Formant
• Discrete cosine transform
• Cepstrum
• Variation
• Chicano English
• William Labov
• Linguistics
• American English
• Phonetics

OVERLAPPED SPEECH DETECTION FOR IMPROVED SPEAKER DIARIZATION IN MULTIPARTY MEETINGS Kofi Boakye1 , Beatriz Trueba-Hornero1,2 , Oriol Vinyals1 , Gerald Friedland1 1 International Computer Science Institute, Berkeley, CA,

Source URL: www.icsi.berkeley.edu

• Human communication
• Computational linguistics
• Science
• Phonetics
• Speech
• Speech recognition
• Mel-frequency cepstrum
• Speech error
• Linear predictive coding
• Automatic identification and data capture
• Speech processing
• Human–computer interaction