Search

Aspects of speech production have provided inspiration for ideas in speech technologies throughout the history of speech processing research. This special issue was inspired by the 2013Workshop on Speech Production in Automatic Speech Recognition in Lyon, France, and this introduction provides an overview of the included papers in the context of the current research landscape. [ABSTRACT FROM AUTHOR]

Spoken language, especially conversational speech, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on conversational speech, and it has been very difficult to mitigate in traditional phone-based approaches to speech recognition. An alternative approach, which has been studied by ourselves and others, is one based on sub-phonetic features rather than phones. In such an approach, a word's pronunciation is represented as multiple streams of phonological features rather than a single stream of phones. Features may correspond to the positions of the speech articulators, such as the lips and tongue, or may be more abstract categories such as manner and place. This article reviews our work on a particular type of articulatory feature-based pronunciation model. The model allows for asynchrony between features, as well as per-feature substitutions, making it more natural to account for many pronunciation changes that are difficult to handle with phone-based models. Such models can be efficiently represented as dynamic Bayesian networks. The feature-based models improve significantly over phone-based counterparts in terms of frame perplexity and lexical access accuracy. The remainder of the article discusses related work and future directions. [ABSTRACT FROM AUTHOR]

We study the problem of automatic visual speech recognition (VSR) using dynamic Bayesian network (DBN)-based models consisting of multiple sequences of hidden states, each corresponding to an articulatory feature (AF) such as lip opening (LO) or lip rounding (LR). A bank of discriminative articulatory feature classifiers provides input to the DBN, in the form of either virtual evidence (VE) (scaled likelihoods) or raw classifier margin outputs. We present experiments on two tasks, a medium-vocabulary word-ranking task and a small-vocabulary phrase recognition task. We show that articulatory feature-based models outperform baseline models, and we study several aspects of the models, such as the effects of allowing articulatory asynchrony, of using dictionary-based versus whole-word models, and of incorporating classifier outputs via virtual evidence versus alternative observation models. [ABSTRACT FROM AUTHOR]

Speech is a critical form of human communication and is central to our daily lives. Yet, despite decades of study, an understanding of the fundamental neural control of speech production remains incomplete. Current theories model speech production as a hierarchy from sentences and phrases down to words, syllables, speech sounds (phonemes), and the actions of vocal tract articulators used to produce speech sounds (articulatory gestures). Here, we investigate the cortical representation of articulatory gestures and phonemes in ventral precentral and inferior frontal gyri in men and women. Our results indicate that ventral precentral cortex represents gestures to a greater extent than phonemes, while inferior frontal cortex represents both gestures and phonemes. These findings suggest that speech production shares a common cortical representation with that of other types of movement, such as arm and hand movements. This has important implications both for our understanding of speech production and for the design of brain-machine interfaces to restore communication to people who cannot speak. [ABSTRACT FROM AUTHOR]

Although much is known about how speech is produced, and research into speech production has resulted in measured articulatory data, feature systems of different kinds, and numerous models, speech production knowledge is almost totally ignored in current mainstream approaches to automatic speech recognition. Representations of speech production allow simple explanations for many phenomena observed in speech which cannot be easily analyzed from either acoustic signal or phonetic transcription alone. In this article, a survey of a growing body of work in which such representations are used to improve automatic speech recognition is provided. [ABSTRACT FROM AUTHOR]

We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL. Recognizing fingerspelling is challenging for a number of reasons: it involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. In this work we collect and annotate a new data set of continuous fingerspelling videos, compare several types of recognizers, and explore the problem of signer variation. Our best-performing models are segmental (semi-Markov) conditional random fields using deep neural network-based features. In the signer-dependent setting, our recognizers achieve up to about 92% letter accuracy. The multi-signer setting is much more challenging, but with neural network adaptation we achieve up to 83% letter accuracies in this setting. [ABSTRACT FROM AUTHOR]

The MIT SUMMIT speech recognition system models pronunciation using a phonemic baseform dictionary along with rewrite rules for modeling phonological variation and multi-word reductions. Each pronunciation component is encoded within a finite-state transducer (FST) representation whose transition weights can be trained using an EM algorithm for finite-state networks. This paper explains the modeling approach we use and the details of its realization, We demonstrate the benefits and weaknesses of the approach both conceptually and empirically using the recognizer for our JUPITER weather information system. Our experiments demonstrate that the use of phonological rewrite rules within our system achieves word error rate reductions between 4% and 9% over different test sets when compared against a system using no phonological rewrite rules. [ABSTRACT FROM AUTHOR]