Multivariate Cepstral Feature Compensation on Band-limited Data for Robust Speech Recognition

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 144-151

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

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Variational Noise Model Composition Through Model Perturbation for Robust Speech Recognition with Time-Varying Background Noise

Abstract This study proposes a novel model composition method to improve speech recognition performance in time-varying background noise conditions. It is suggested that each element of the cepstral coefficients represents the frequency degree of the changing components in the envelope of the log-spectrum. With this motivation, in the proposed method, variational noise models are formulated by selectively applying perturbation factors to the mean parameters of a basis model, resulting in a collection of noise models that more accurately reflect the natural range of spectral patterns seen in the log-spectral domain. The basis noise model is obtained from the silence segments of the input speech. The perturbation factors are designed separately for changes in the energy level and spectral envelope. The proposed variational model composition (VMC) method is employed to generate multiple environmental models for our previously proposed parallel combined gaussian mixture model (PCGMM) based feature compensation algorithm. The mixture sharing technique is integrated to reduce computational expenses, caused by employing the variational models. Experimental results prove that the proposed method is considerably more effective at increasing speech recognition performance in time-varying background noise conditions, with +31.31%, +10.65%, and +20.54% average relative improvements in word error rate for speech babble, background music, and real-life in-vehicle noise conditions respectively, compared to the original basic PCGMM method

Phoneme-based Video Indexing Using Phonetic Disparity Search

This dissertation presents and evaluates a method to the video indexing problem by investigating a categorization method that transcribes audio content through Automatic Speech Recognition (ASR) combined with Dynamic Contextualization (DC), Phonetic Disparity Search (PDS) and Metaphone indexation. The suggested approach applies genome pattern matching algorithms with computational summarization to build a database infrastructure that provides an indexed summary of the original audio content. PDS complements the contextual phoneme indexing approach by optimizing topic seek performance and accuracy in large video content structures. A prototype was established to translate news broadcast video into text and phonemes automatically by using ASR utterance conversions. Each phonetic utterance extraction was then categorized, converted to Metaphones, and stored in a repository with contextual topical information attached and indexed for posterior search analysis. Following the original design strategy, a custom parallel interface was built to measure the capabilities of dissimilar phonetic queries and provide an interface for result analysis. The postulated solution provides evidence of a superior topic matching when compared to traditional word and phoneme search methods. Experimental results demonstrate that PDS can be 3.7% better than the same phoneme query, Metaphone search proved to be 154.6% better than the same phoneme seek and 68.1 % better than the equivalent word search