69 research outputs found
Studies on noise robust automatic speech recognition
Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK
Deep Learning for Distant Speech Recognition
Deep learning is an emerging technology that is considered one of the most
promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech
represents a crucial leap towards intelligent machines. Despite the great
efforts of the past decades, however, a natural and robust human-machine speech
interaction still appears to be out of reach, especially when users interact
with a distant microphone in noisy and reverberant environments. The latter
disturbances severely hamper the intelligibility of a speech signal, making
Distant Speech Recognition (DSR) one of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques,
architectures, and algorithms to improve the robustness of distant-talking
acoustic models. We first elaborate on methodologies for realistic data
contamination, with a particular emphasis on DNN training with simulated data.
We then investigate on approaches for better exploiting speech contexts,
proposing some original methodologies for both feed-forward and recurrent
neural networks. Lastly, inspired by the idea that cooperation across different
DNNs could be the key for counteracting the harmful effects of noise and
reverberation, we propose a novel deep learning paradigm called network of deep
neural networks. The analysis of the original concepts were based on extensive
experimental validations conducted on both real and simulated data, considering
different corpora, microphone configurations, environments, noisy conditions,
and ASR tasks.Comment: PhD Thesis Unitn, 201
Speech processing using digital MEMS microphones
The last few years have seen the start of a unique change in microphones for consumer
devices such as smartphones or tablets. Almost all analogue capacitive microphones
are being replaced by digital silicon microphones or MEMS microphones.
MEMS microphones perform differently to conventional analogue microphones. Their
greatest disadvantage is significantly increased self-noise or decreased SNR, while
their most significant benefits are ease of design and manufacturing and improved sensitivity
matching.
This thesis presents research on speech processing, comparing conventional analogue
microphones with the newly available digital MEMS microphones. Specifically, voice
activity detection, speaker diarisation (who spoke when), speech separation and speech
recognition are looked at in detail.
In order to carry out this research different microphone arrays were built using digital
MEMS microphones and corpora were recorded to test existing algorithms and devise
new ones. Some corpora that were created for the purpose of this research will be
released to the public in 2013.
It was found that the most commonly used VAD algorithm in current state-of-theart
diarisation systems is not the best-performing one, i.e. MLP-based voice activity
detection consistently outperforms the more frequently used GMM-HMM-based VAD
schemes. In addition, an algorithm was derived that can determine the number of active
speakers in a meeting recording given audio data from a microphone array of known
geometry, leading to improved diarisation results.
Finally, speech separation experiments were carried out using different post-filtering
algorithms, matching or exceeding current state-of-the art results.
The performance of the algorithms and methods presented in this thesis was verified
by comparing their output using speech recognition tools and simple MLLR adaptation
and the results are presented as word error rates, an easily comprehensible scale.
To summarise, using speech recognition and speech separation experiments, this thesis
demonstrates that the significantly reduced SNR of the MEMS microphone can be
compensated for with well established adaptation techniques such as MLLR. MEMS
microphones do not affect voice activity detection and speaker diarisation performance
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