Noise Robust Automatic Speech Recognition Based on Spectro-Temporal Techniques

Kovács György
Noise Robust Automatic Speech Recognition Based on Spectro-Temporal Techniques.
Doctoral thesis (PhD), University of Szeged.
(2018) (Unpublished)

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Abstract in foreign language

Speech technology today has a wide variety of existing and potential applications in so many areas of our life. From dictating systems to voice translation, from digital assistants like Siri, Google Now, and Cortana, to telephone dialogue systems. Many of these applications have to rely on an Automatic Speech Recognition (ASR) component. This component not only has to perform well, but it also has to perform well in adverse environments. After all, a dictating system which requires that we insulate our office, or a digital assistant that cannot work in traffic, or in a room full of chatting people is not so helpful. For this reason, noise robust ASR has been a topic of intensive research. Yet, human-equivalent performance has not been achieved. This motivated many to search for ways to improve the robustness of automatic speech recognition based on human speech perception. One popular method inspired by the examination of the receptive fields of auditory neurons is that of spectro-temporal processing. In spectro-temporal processing, the aim is to capture the spectral and temporal modulations of the signal simultaneously. One simple way to do so is to extract the features to be used from spectro-temporal patches, and then use the resulting features in the same manner one would use traditional features like MFCCs. There is more than one way to bake a cake, however. And in this case this is true twice over. For one, there are various ways to extract our features from the patches. But there are other, more sophisticated ways to incorporate the concept of spectro-temporal processing into a speech recognition system. In this study we examine many such methods -- some simpler, some more sophisticated, but all stemming from the same basic idea. By the end of this study we will demonstrate that these methods can indeed lead to more robust speech recognition. So much so, that they can provide results that are competitive with the state-of-the-art results.

Item Type: Thesis (Doctoral thesis (PhD))
Creators: Kovács György
Hungarian title: Spektro-temporális feldolgozási módszereken alapuló zajtűrő automatikus beszédfelismerés
Position, academic title, institution
MTMT author ID
Tóth László
tudományos főmunkatárs, PhD, SZTE TTIK ITCS Számítógépes Algoritmusok és Mesterséges Intelligencia Tanszék
Van Compernolle Dirk
professzor, PhD, KU Leuven ESAT - PSI, Processing Speech and Images
Subjects: 01. Natural sciences > 01.01. Mathematics
Divisions: Doctoral School of Computer Science
Discipline: Natural Sciences > Mathematics and Computer Sciences
Language: English
Date: 2018. March 29.
Item ID: 4108
MTMT identifier of the thesis: 3402361
Date Deposited: 2017. Oct. 16. 08:41
Last Modified: 2020. May. 29. 10:40
Depository no.: B 6347
Defence/Citable status: Defended.

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