The Unsupervised Acquisition of a Lexicon from Continuous Speech

We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of symbol sequences in an MDL framework, and uses a hierarchical representation of language that overcomes many of the problems that have stymied previous grammar-induction procedures. The forward mapping from symbol sequences to the speech stream is modeled using features based on articulatory gestures. We present results on the acquisition of lexicons and language models from raw speech, text, and phonetic transcripts, and demonstrate that our algorithm compares very favorably to other reported results with respect to segmentation performance and statistical efficiency.Comment: 27 page technical repor

Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic

Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint morphological-lexical language model (JMLLM) that takes advantage of Arabic morphology. JMLLM combines morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items in a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates while keeping the predictive power of whole words. Speech recognition and machine translation experiments in dialectal-Arabic show improvements over word and morpheme based trigram language models. We also show that as the tightness of integration between different information sources increases, both speech recognition and machine translation performances improve

Saliency or template? ERP evidence for long-term representation of word stress

The present study investigated the event-related brain potential (ERP) correlates of word stress processing. Previous results showed that the violation of a legal stress pattern elicited two consecutive Mismatch Negativity (MMN) components synchronized to the changes on the first and second syllable. The aim of the present study was to test whether ERPs reflect only the detection of salient features present on the syllables, or they reflect the activation of long-term stress related representations. We examined ERPs elicited by pseudowords with no lexical representation in two conditions: the standard having a legal stress patterns, and the deviant an illegal one, and the standard having an illegal stress pattern, and the deviant a legal one. We found that the deviant having an illegal stress pattern elicited two consecutive MMN components, whereas the deviant having a legal stress pattern did not elicit MMN. Moreover, pseudowords with a legal stress pattern elicited the same ERP responses irrespective of their role in the oddball sequence, i.e., if they were standards or deviants. The results suggest that stress pattern changes are processed relying on long-term representation of word stress. To account for these results, we propose that the processing of stress cues is based on language-specific, pre-lexical stress templates

Statistical Augmentation of a Chinese Machine-Readable Dictionary

We describe a method of using statistically-collected Chinese character groups from a corpus to augment a Chinese dictionary. The method is particularly useful for extracting domain-specific and regional words not readily available in machine-readable dictionaries. Output was evaluated both using human evaluators and against a previously available dictionary. We also evaluated performance improvement in automatic Chinese tokenization. Results show that our method outputs legitimate words, acronymic constructions, idioms, names and titles, as well as technical compounds, many of which were lacking from the original dictionary.Comment: 17 pages, uuencoded compressed PostScrip

Linguistic constraints on statistical word segmentation: The role of consonants in Arabic and English

Statistical learning is often taken to lie at the heart of many cognitive tasks, including the acquisition of language. One particular task in which probabilistic models have achieved considerable success is the segmentation of speech into words. However, these models have mostly been tested against English data, and as a result little is known about how a statistical learning mechanism copes with input regularities that arise from the structural properties of different languages. This study focuses on statistical word segmentation in Arabic, a Semitic language in which words are built around consonantal roots. We hypothesize that segmentation in such languages is facilitated by tracking consonant distributions independently from intervening vowels. Previous studies have shown that human learners can track consonant probabilities across intervening vowels in artificial languages, but it is unknown to what extent this ability would be beneficial in the segmentation of natural language. We assessed the performance of a Bayesian segmentation model on English and Arabic, comparing consonant-only representations with full representations. In addition, we examined to what extent structurally different proto-lexicons reflect adult language. The results suggest that for a child learning a Semitic language, separating consonants from vowels is beneficial for segmentation. These findings indicate that probabilistic models require appropriate linguistic representations in order to effectively meet the challenges of language acquisition

Evaluating dictation task measures for the study of speech perception

This paper shows that the dictation task, a well- known testing instrument in language education, has untapped potential as a research tool for studying speech perception. We describe how transcriptions can be scored on measures of lexical, orthographic, phonological, and semantic similarity to target phrases to provide comprehensive information about accuracy at different processing levels. The former three measures are automatically extractable, increasing objectivity, and the middle two are gradient, providing finer-grained information than traditionally used. We evaluate the measures in an English dictation task featuring phonetically reduced continuous speech. Whereas the lexical and orthographic measures emphasize listeners’ word identification difficulties, the phonological measure demonstrates that listeners can often still recover phonological features, and the semantic measure captures their ability to get the gist of the utterances. Correlational analyses and a discussion of practical and theoretical considerations show that combining multiple measures improves the dictation task’s utility as a research tool