Privacy-Sensitive Audio Features for Speech/Nonspeech Detection
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The goal of this thesis is to develop and design new feature representations that can improve the automatic speech recognition (ASR) performance in clean as well noisy conditions. One of the main shortcomings of the fixed scale (typically 20-30 ms long ana ...
Automatic speech recognition (ASR) systems, trained on speech signals from close-talking microphones, generally fail in recognizing far-field speech. In this paper, we present a Hilbert Envelope based feature extraction technique to alleviate the artifacts ...
In a previous paper on speech recognition, we showed that templates can better capture the dynamics of speech signal compared to parametric models such as hidden Markov models. The key point in template matching approaches is finding the most similar templ ...
Modern speech recognition has many ways of quantifying the misrecognitions a speech recognizer makes. The errors in modern speech recognition makes extensive use of the Levenshtein algorithm to find the distance between the labeled target and the recognize ...
In this work, we propose different strategies for efficiently integrating an automated speech recognition module in the framework of a dialogue-based vocal system. The aim is the study of different ways leading to the improvement of the quality and robustn ...
Performance of a typical automatic speech recognition (ASR) system severely degrades when it encounters speech from reverberant environments. Part of the reason for this degradation is the feature extraction techniques that use analysis windows which are m ...
We address issues for improving hands-free speech recognition performance in the presence of multiple simultaneous speakers using multiple distant microphones. In this paper, a log spectral mapping is proposed to estimate the log mel-filterbank outputs of ...
Performance of a typical automatic speech recognition (ASR) system severely degrades when it encounters speech from reverberant environments. Part of the reason for this degradation is the feature extraction techniques that use analysis windows which are m ...
In this paper, we present a novel feature normalization method in the log-scaled spectral domain for improving the noise robustness of speech recognition front-ends. In the proposed scheme, a non-linear contrast stretching is added to the outputs of log me ...
In this paper, we present a novel feature normalization method in the log-scaled spectral domain for improving the noise robustness of speech recognition front-ends. In the proposed scheme, a non-linear contrast stretching is added to the outputs of log me ...
