Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Continuous Speech Recognition using Boosted Binary Features

A novel parts-based binary-valued feature termed Boosted Binary Feature (BBF) was recently proposed for ASR. Such features look at specific pairs of time-frequency bins in the spectro-temporal plane. The most discriminative of these features are selected by boosting and integrated into a standard HMM-based system using multilayer perceptron (MLP) and single layer perceptron (SLP). Previous studies on TIMIT phoneme recognition task showed that BBF yields similar or better performance compared to cepstral features. In this work, this study is extended to continuous speech recognition task on the DARPA Resource Management database. Results show that BBF achieves comparable word error rate (5.5%) on this task with respect to standard cepstral features (5.1%) using MLP. Using SLP, the error rate for BBF shows much lower degradation (from 5.5% to 7.1%) compared to cepstral features (from 5.1% to 14.7%). In addition, it is found that BBF features can be selected well using auxiliary data

Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition

This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network