Tactual Display of Consonant Voicing to Supplement Lipreading

This research is concerned with the development of tactual displays to supplement the information available through lipreading. Because voicing carries a high informational load in speech and is not well transmitted through lipreading, the efforts are focused on providing tactual displays of voicing to supplement the information available on the lips of the talker. This research includes exploration of 1) signal-processing schemes to extract information about voicing from the acoustic speech signal, 2) methods of displaying this information through a multi-finger tactual display, and 3) perceptual evaluations of voicing reception through the tactual display alone (T), lipreading alone (L), and the combined condition (L+T). Signal processing for the extraction of voicing information used amplitude-envelope signals derived from filtered bands of speech (i.e., envelopes derived from a lowpass-filtered band at 350 Hz and from a highpass-filtered band at 3000 Hz). Acoustic measurements made on the envelope signals of a set of 16 initial consonants represented through multiple tokens of C1VC2 syllables indicate that the onset-timing difference between the low- and high-frequency envelopes (EOA: envelope-onset asynchrony) provides a reliable and robust cue for distinguishing voiced from voiceless consonants. This acoustic cue was presented through a two-finger tactual display such that the envelope of the high-frequency band was used to modulate a 250-Hz carrier signal delivered to the index finger (250-I) and the envelope of the low-frequency band was used to modulate a 50-Hz carrier delivered to the thumb (50T). The temporal-onset order threshold for these two signals, measured with roving signal amplitude and duration, averaged 34 msec, sufficiently small for use of the EOA cue. Perceptual evaluations of the tactual display of EOA with speech signal indicated: 1) that the cue was highly effective for discrimination of pairs of voicing contrasts; 2) that the identification of 16 consonants was improved by roughly 15 percentage points with the addition of the tactual cue over L alone; and 3) that no improvements in L+T over L were observed for reception of words in sentences, indicating the need for further training on this task

A Computational Model for the Acquisition and Use of Phonological Knowledge

Does knowledge of language consist of symbolic rules? How do children learn and use their linguistic knowledge? To elucidate these questions, we present a computational model that acquires phonological knowledge from a corpus of common English nouns and verbs. In our model the phonological knowledge is encapsulated as boolean constraints operating on classical linguistic representations of speech sounds in term of distinctive features. The learning algorithm compiles a corpus of words into increasingly sophisticated constraints. The algorithm is incremental, greedy, and fast. It yields one-shot learning of phonological constraints from a few examples. Our system exhibits behavior similar to that of young children learning phonological knowledge. As a bonus the constraints can be interpreted as classical linguistic rules. The computational model can be implemented by a surprisingly simple hardware mechanism. Our mechanism also sheds light on a fundamental AI question: How are signals related to symbols?

Computational Structure of Human Language

The central thesis of this report is that human language is NP-complete. That is, the process of comprehending and producing utterances is bounded above by the class NP, and below by NP-hardness. This constructive complexity thesis has two empirical consequences. The first is to predict that a linguistic theory outside NP is unnaturally powerful. The second is to predict that a linguistic theory easier than NP-hard is descriptively inadequate. To prove the lower bound, I show that the following three subproblems of language comprehension are all NP-hard: decide whether a given sound is possible sound of a given language; disambiguate a sequence of words; and compute the antecedents of pronouns. The proofs are based directly on the empirical facts of the language user's knowledge, under an appropriate idealization. Therefore, they are invariant across linguistic theories. (For this reason, no knowledge of linguistic theory is needed to understand the proofs, only knowledge of English.) To illustrate the usefulness of the upper bound, I show that two widely-accepted analyses of the language user's knowledge (of syntactic ellipsis and phonological dependencies) lead to complexity outside of NP (PSPACE-hard and Undecidable, respectively). Next, guided by the complexity proofs, I construct alternate linguisitic analyses that are strictly superior on descriptive grounds, as well as being less complex computationally (in NP). The report also presents a new framework for linguistic theorizing, that resolves important puzzles in generative linguistics, and guides the mathematical investigation of human language.

Restructuring Sparse High Dimensional Data for Effective Retrieval

The task in text retrieval is to find the subset of a collection of documents relevant to a user's information request, usually expressed as a set of words. Classically, documents and queries are represented as vectors of word counts. In its simplest form, relevance is defined to be the dot product between a document and a query vector--a measure of the number of common terms. A central difficulty in text retrieval is that the presence or absence of a word is not sufficient to determine relevance to a query. Linear dimensionality reduction has been proposed as a technique for extracting underlying structure from the document collection. In some domains (such as vision) dimensionality reduction reduces computational complexity. In text retrieval it is more often used to improve retrieval performance. We propose an alternative and novel technique that produces sparse representations constructed from sets of highly-related words. Documents and queries are represented by their distance to these sets. and relevance is measured by the number of common clusters. This technique significantly improves retrieval performance, is efficient to compute and shares properties with the optimal linear projection operator and the independent components of documents.

Leveraging Learning and Language Via Communication Bootstrapping

In a Communication Bootstrapping system, peer components with different perceptual worlds invent symbols and syntax based on correlations between their percepts. I propose that Communication Bootstrapping can also be used to acquire functional definitions of words and causal reasoning knowledge. I illustrate this point with several examples, then sketch the architecture of a system in progress which attempts to execute this task.

Naive Physics, Event Perception, Lexical Semantics, and Language Acquisition

This thesis proposes a computational model of how children may come to learn the meanings of words in their native language. The proposed model is divided into two separate components. One component produces semantic descriptions of visually observed events while the other correlates those descriptions with co-occurring descriptions of those events in natural language. The first part of this thesis describes three implementations of the correlation process whereby representations of the meanings of whole utterances can be decomposed into fragments assigned as representations of the meanings of individual words. The second part of this thesis describes an implemented computer program that recognizes the occurrence of simple spatial motion events in simulated video input.