Speech recognition in mobile environment Robust ASR with dual Mic UNIVERSITE D’ORAN 1 Ahmed Ben Bella Speech recognition in mobile environment Robust ASR with dual Mic Présenté par : Yacine IKKACHE Encadré par : Pr. Med SENOUCI Dr. B KOUNINEF
WHAT IS ASR Command and control Automatic transcription Automatic translation Home automation Voice dialing
How its work
HMM-Based Recognizer pattern classification Mathematical Formulation:
HMM-Based Recognizer pattern classification acoustic model
HMM-Based Recognizer pattern classification acoustic model
HMM-Based Recognizer pattern classification language model
HMM-Based Recognizer pattern classification search problem
Building Quran reader controlled by speech ASR with sphinx Sphinx4 is a software implementation of HMM speech recognizer, it’s architecture is highly flexible
Acoustic model for Quranic reader data collection Speech collection We prepared a text file which contain 114 suras name’s, famous receiters names
Acoustic model for Quranic reader data collection The audio file was recorded using a sampling rate of 16KHZ and 16 bit per sample Each file has been named using this convention: speakername-commandID.wav These audio files were divided into two sets
Building Quran reader controlled
Building Quran reader controlled
Publication "Building Quranic reader voice interface using sphinx toolkit" in the Journal of American sciences (novembre 2013) "Toward Quranic reader controlled by speech" in international journal of Advanced Computer Science & Application ( avril 2012) The audio file was recorded using a sampling rate of 16KHZ and 16 bit per sample Each file has been named using this convention: speakername-commandID.wav These audio files were divided into two sets
Speech recognition in mobile environment
Speech recognition in mobile environment Architecture The decision is driven by factors including device and network resources, ASR components complexity and application.
Speech recognition in mobile environment NSR Coding Transmission errors
Speech recognition in mobile environment DSR The absence of coding and transcoding problems Robustness against comm. channel & acoustic noise Thin client, easy to update, no limits in ASR complexity Front-end must be implemented in the device Network dependency and transmission errors
Robust speech recognition on mobile environments. Main research lines of the group: Robust speech recognition on mobile environments. Robust ASR on mobile devices with small microphone array. Robust transmission of speech and video. Ultrasonic non-destructive testing. Signal processing in proteomics.
Robust speech recognition on mobile environments.
Robust speech recognition on mobile environments.
Robust speech recognition on mobile environments.
Robust speech recognition on mobile environments.
Robust speech recognition on mobile environments.
Robust speech recognition on mobile environments.
Robust speech recognition on mobile environments Robust speech recognition on mobile environments. Noise reduction with single microphone
Robust speech recognition on mobile environments Robust speech recognition on mobile environments. Noise reduction with dual Mic
Robust speech recognition on mobile environments Robust speech recognition on mobile environments. Noise reduction with dual mic
Robust speech recognition on mobile environments Robust speech recognition on mobile environments. Noise reduction with dual mic
Noise reduction with dual mic DNN to extract binary mask Marginilization Frame reconstruction
Noise reduction with dual mic DNN to extract soft mask Y’= ES * Y1
Noise reduction with dual mic Dual Mic database creation
conclusion Multichannel information can be exploited to improve ASR performance. We are working on implementing novel technique ( DNN based soft mask estimation for robust ASR in Matlab ) The extracted features will be used in sphinx for recognition
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