Jeung-Yoon Choi

Maria-Gabriella Di Benedetto, Stefanie Shattuck-Hufnagel, Jeung-Yoon Choi et al.

Modelling the process that a listener actuates in deriving the words intended by a speaker requires setting a hypothesis on how lexical items are stored in memory. This work aims at developing a system that imitates humans when identifying words in running speech and, in this way, provide a framework to better understand human speech processing. We build a speech recognizer for Italian based on the principles of Stevens' model of Lexical Access in which words are stored as hierarchical arrangements of distinctive features (Stevens, K. N. (2002). "Toward a model for lexical access based on acoustic landmarks and distinctive features," J. Acoust. Soc. Am., 111(4):1872-1891). Over the past few decades, the Speech Communication Group at the Massachusetts Institute of Technology (MIT) developed a speech recognition system for English based on this approach. Italian will be the first language beyond English to be explored; the extension to another language provides the opportunity to test the hypothesis that words are represented in memory as a set of hierarchically-arranged distinctive features, and reveal which of the underlying mechanisms may have a language-independent nature. This paper also introduces a new Lexical Access corpus, the LaMIT database, created and labeled specifically for this work, that will be provided freely to the speech research community. Future developments will test the hypothesis that specific acoustic discontinuities - called landmarks - that serve as cues to features, are language independent, while other cues may be language-dependent, with powerful implications for understanding how the human brain recognizes speech.

Xiang Kong, Jeung-Yoon Choi, Stefanie Shattuck-Hufnagel

This paper describes methods for evaluating automatic speech recognition (ASR) systems in comparison with human perception results, using measures derived from linguistic distinctive features. Error patterns in terms of manner, place and voicing are presented, along with an examination of confusion matrices via a distinctive-feature-distance metric. These evaluation methods contrast with conventional performance criteria that focus on the phone or word level, and are intended to provide a more detailed profile of ASR system performance,as well as a means for direct comparison with human perception results at the sub-phonemic level.

Xiang Kong, Xuesong Yang, Mark Hasegawa-Johnson et al.

This paper tests the hypothesis that distinctive feature classifiers anchored at phonetic landmarks can be transferred cross-lingually without loss of accuracy. Three consonant voicing classifiers were developed: (1) manually selected acoustic features anchored at a phonetic landmark, (2) MFCCs (either averaged across the segment or anchored at the landmark), and(3) acoustic features computed using a convolutional neural network (CNN). All detectors are trained on English data (TIMIT),and tested on English, Turkish, and Spanish (performance measured using F1 and accuracy). Experiments demonstrate that manual features outperform all MFCC classifiers, while CNNfeatures outperform both. MFCC-based classifiers suffer an F1reduction of 16% absolute when generalized from English to other languages. Manual features suffer only a 5% F1 reduction,and CNN features actually perform better in Turkish and Span-ish than in the training language, demonstrating that features capable of representing long-term spectral dynamics (CNN and landmark-based features) are able to generalize cross-lingually with little or no loss of accuracy