3 research outputs found
Channel Selection for Distant Speech Recognition Exploiting Cepstral Distance
In a multi-microphone distant speech recognition task, the redundancy of information that results from the availability of multiple instances of the same source signal can be exploited through channel selection. In this work, we propose the use of cepstral distance as a means of assessment of the available channels, in an informed and a blind fashion. In the informed approach the distances between the close-talk and all of the channels are calculated. In the blind method, the cepstral distances are computed using an estimated reference signal, assumed to represent the average distortion among the available channels. Furthermore, we propose a new evaluation methodology that better illustrates the strengths and weaknesses of a channel selection method, in comparison to the sole use of word error rate. The experimental results suggest that the proposed blind method successfully selects the least distorted channel, when sufficient room coverage is provided by the microphone network. As a result, improved recognition rates are obtained in a distant speech recognition task, both in a simulated and a real context
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