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An Overview of Noise-Robust Automatic Speech …
https://www.microsoft.com/en-us/research/publication/an-overview-of-noise-robust-automatic-speech-recognition/
New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and …
Robust Self-Supervised Audio-Visual Speech Recognition
https://arxiv.org/abs/2201.01763
Audio-based automatic speech recognition (ASR) degrades significantly in noisy environments and is particularly vulnerable to interfering speech, as the model cannot determine which speaker to transcribe. Audio-visual speech recognition (AVSR) systems improve robustness by complementing the audio stream with the visual information that is invariant to …
Robust Self-Supervised Audio-Visual Speech Recognition ...
https://paperswithcode.com/paper/robust-self-supervised-audio-visual-speech
Audio-visual speech recognition (AVSR) systems improve robustness by complementing the audio stream with the visual information that is invariant to noise and helps the model focus on the desired speaker. .. However, previous AVSR work focused solely on the supervised learning setup; hence the progress was hindered by the amount of labeled data ...
ROBUST AUDIO-VISUAL SPEECH RECOGNITION …
http://lxie.npu-aslp.org/papers/2019ICASSP-ShiliangZhang.pdf
Audio-visual speech recognition (AVSR) is thought to be one of the potential solutions for robust speech recognition, especially in noisy environments. Compared to audio only speech recognition, the major issues of AVSR include the lack of publicly available audio-visual corpora and the need of robust knowledge fusion of both speech and vision.
Robust Speaker Recognition
http://www.lti.cs.cmu.edu/sites/default/files/research/thesis/2007/qin_jin_robust_speaker_recognition.pdf
Such high-level information is expected to be robust under different mismatched conditions. We also built systems that support robust speaker recognition. We implemented a speaker segmentation and clustering system aiming at improving the robustness of speaker recognition as well as automatic speech recognition performance in the multiple-
A Robust Environmental Sound Recognition System using ...
https://research.ijcaonline.org/volume80/number9/pxc3891800.pdf
features that can be used, or are needed, to describe audio signals. The appropriate choice of these features is crucial in building a robust recognition system. A considerable number of audio features are used in this project from frequency-domain (spectral). 3.2.1 FREQUENCY-DOMAIN FEATURES 3.2.1.1 Spectral Skewness
Real-time Robust Recognition of Speakers’ Emotions and ...
https://scholar.harvard.edu/files/bhb/files/opensmile.pdf
RNN) is combined with our core emotion recognition and speaker characterisation engine natively on the mobile device. This eliminates the need for network connectivity and allows to perform robust speaker state and trait recognition efficiently in real-time without network transmission lags. Real-time factors
Noise-Robust Speech Recognition in a Car Environment …
https://www.tytlabs.com/english/review/rev391epdf/e391_004hoshino.pdf
speech recognition have been widely adopted for in-vehicle information equipment such as navigation systems. One of the greatest problems facing speech recognition in car environments is the degradation of the recognition performance as a result of car interior noise while driving. Many research projects into noise-robust speech recognition in car
Automatic Speech Processing | GAMMA
https://gamma.umd.edu/researchdirections/speech/main
Overview. Artificial reverberation has been added to anechoic speech data to train more robust machine learning models for automatic speech processing. We are developing methods for automatic speech recognition, source separation and localization, binaural audio generation, and speech emotion recognition. Software.
Introduction to Automatic Speech Recognition (ASR)
https://maelfabien.github.io/machinelearning/speech_reco/
Automatic Speech Recognition (ASR), or Speech-to-text (STT) is a field of study that aims to transform raw audio into a sequence of corresponding words. Some of the speech-related tasks involve: speaker diarization: which speaker spoke when?
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