NASA Scientific and Technical Aerospace Reports

discrimination information (or the cross-entropy) between the source and the model is proposed. This approach does not
require the commonly used assumption that the source to be modeled is a hidden Markov process. The algorithm is started
from the model estimated by the traditional maximum likelihood (ML) approach and alternatively decreases the discrimination
information over all probability distributions of the source which agree with the given measurements and all hidden Markov
models. The proposed procedure generalizes the Baum algorithm for ML hidden Markov modeling. The procedure is shown
to be a descent algorithm for the discrimination information measure and its local convergence is proved.
Author
Markov Processes; Information Theory; Information Systems; Probability Distribution Functions; Maximum Likelihood
Estimates
20060001627 International Business Machines Corp., Paris, France
Context-Dependent Phonetic Markov Models for Large Vocabulary Speech Recognition
Derouault, Anne-Marie; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume
1; 1987, pp. 10.1.1 - 10.1.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
One approach to large vocabulary speech recognition, is to build phonetic Markov models, and to concatenate them to
obtain word models. In previous work, we already designed a recognizer based on 40 phonetic Markov machines, which
accepts a 10,000 words vocabulary ([3]), and recently 200,000 words vocabulary ([5]). Since there is one machine per
phoneme, these models obviously do not account for coarticulatory effects, which may lead to recognition errors. In this paper,
we improve the phonetic models by using general principles about coarticulation effects on automatic phoneme recognition.
We show that both the analysis of the errors made by the recognizer, and linguistic facts about phonetic context influence,
suggest a method for choosing context dependent models. This method allows to limit the growing of the number of phonems,
and still account for the most important coarticulation effects. We present our experiments with a system applying these
principles to a set of models for French. With this new system including context-dependent machines, the phoneme recognition
rate goes from 82.2% to 85.3%, and the error rate on words with a 10,000 word dictionary, is decreased from 11.2 to 9.8%.
Author
Context; Phonemes; Error Analysis; Phonetics; Words (Language); Speech Recognition; Linguistics
20060001657 Mitre Corp., McLean, VA, USA
Information-Theoretic Compressibility of Speech Data
Ramsey, L. Thomas; Gribble, David; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP
‘87); Volume 1; 1987, pp. 1.6.1 - 1.6.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
Two standard reversible coding algorithms, Ziv-Lempel and a dynamic Huffman algorithm, are applied to various types
of speech data. The data tested were PCM, DPCM, and prediction residuals from LPC. Neither algorithm shows much promise
on small amounts of data, but both performed well on large amounts. Typically the Ziv-Lempel required about 12 seconds of
data (with 8000 samples per second) to reach a stable compression rate. The dynamic Huffman coding took much less time
to warm up’, often needing something like 64 milliseconds. Approximately 66 seconds of PCM with 12 bits per samples was
compressed 6.4% by the Ziv-Lempel coding and 20.7% by the dynamic Huffman coding. The same numbers for DPCM with
13 bits per sample are 17.7% and 35.6% respectively. The prediction residuals had compression rates very close to those of
DPCM, regardless of whether 1, 2, 5, or 10 prediction coefficients were used.
Author
Information Theory; Compressibility; Predictions; Speech; Coeffıcients; Differential Pulse Code Modulation
20060001668 American Telephone and Telegraph Co., NJ, USA
A Connected Speech Recognition System Based on Spotting Diphone-Like Segments - Preliminary Results
Rosenberg, A. E.; Colla, A. M.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87);
Volume 1; 1987, pp. 3.6.1-3.6.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
A template-based connected speech recognition system, which represents words as sequences of diphone-like segments,
has been implemented and evaluated. The inventory of segments is divided into two principal classes: ‘steady-state’ speech
sounds such as vowels, fricatives, and nasals, and ‘composite’ speech sounds consisting of sequences of two or more speech
sounds in which the transitions from one sound to another are intrinsic to the representation of the composite sound. Templates
representing these segments are extracted from labelled training utterances. Words are represented by network models whose
branches are diphone segments. Word juncture phenomena are accommodated by including segment branches that characterize
transition pronunciations between specified classes of words. The recognition of a word in a specified utterance takes place
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