NASA Scientific and Technical Aerospace Reports

y ‘spotting’ all the segments contained in the model of the word. Putative words and word combinations are found by
searching for best scoring sequences of segments specified by the models subject to segment separation constraints. A pruning
procedure finds the best scoring string of words subject to constraints on word lengths. separations, and overlaps. An
evaluation of the recognizer has been carried out on a database of connected digit utterances spoken by a single male talker.
Templates are extracted from half the database consisting of 2100 digit utterances and system performance tested on the
remaining 2100 utterances. The performance obtained to date is approximately 2% digit error rate and 7 to 8% digit string error
rate.
Author
Speech Recognition; Words (Language); Vowels; Speech; Sequencing
20060001670 BBN Systems and Technologies Corp., Cambridge, MA, USA
BYBLOS: The BNN Continuous Speech Recognition System
Chow, Y.L.; Dunham, M.O.; Kimball, O. A.; Krasner, M. A.; Kubala, G. F.; Makhoul, J.; Price, P. J.; Roucos, S.; Schwartz,
R. M.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1; 1987,
pp. 3.7.1-3.7.4; In English; See also 20060001583
Contract(s)/Grant(s): N00039-85-C-0423; Copyright; Avail.: Other Sources
In this paper, we describe BYBLOS, the BBN continuous speech recognition system. The system, designed for large
vocabulary applications, integrates acoustic, phonetic, lexical, and linguistic knowledge sources to achieve high recognition
performance. The basic approach, as described in previous papers, makes extensive use of robust context-dependent models
of phonetic coarticulation using Hidden Markov Models (HMM). We describe the components of the BYBLOS system,
including: signal processing frontend, dictionary, phonetic model training system, word model generator, grammar and
decoder. In recognition experiments, we demonstrate consistently high word recognition performance on continuous speech
across: speakers, task domains, and grammars of varying complexity. In speaker-dependent mode, where 15 minutes of speech
is required for training to a speaker, 98.5% word accuracy has been achieved in continuous speech for a 350-word task, using
grammars with perplexity ranging from 30 to 60. With only 15 seconds of training speech we demonstrate performance of 97%
using a grammar.
Author
Signal Processing; Speech Recognition; Phonetics; Linguistics; Grammars; Decoders
20060001673 American Telephone and Telegraph Co., USA
Performance Evaluation of a Connected Digit Recognizer
Rabiner, L. R.; Wilpon, J. G.; Juang, B. H.; IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP ‘87); Volume 1; 1987, pp. 3.10.1-3.10.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
In this paper we discuss a system for automatically recognizing fluently spoken digit strings based on whole word
reference units. The system that we will describe can use either hidden Markov model (HMM) technology or template-based
technology. The training procedure derives the digit reference patterns (either templates or statistical models) from connected
digit strings. To evaluate the performance of the overall connected digit recognizer, a set of 50 people (25 men, 25 women),
from the non-technical local population, was each asked to record 1200 random connected digit strings over local dialed-up
telephone lines. Both a speaker trained and a multispeaker training set was created, and a full performance evaluation was
made. Results show that the average string accuracy for unknown and known length strings, in the speaker trained mode, was
98% and 99% respectively; in the multi-speaker mode the average string accuracies were 94% and 96.6% respectively.
Author
Performance Tests; Templates; Evaluation; Mathematical Models; Speech
20060001738 International Business Machines Corp., Paris, France
Speech Recognition with Very Large Size Dictionary
Merialdo, Bernard; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1;
1987, pp. 10.2.1 - 10.2.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
This paper proposes a new strategy, the Multi-Level Decoding (MLD), that allows to use a Very Large Size Dictionary
(VLSD, size more than 100,000 words) in speech recognition. MLD proceeds in three steps: 1) a Syllable Match procedure
uses an acoustic model to build a list of the most probable syllables that match the acoustic signal from a given time frame.
2) from this list, a Word Match procedure uses the dictionary to build partial word hypothesis. 3) then a Sentence Match
procedure uses a probabilistic language model to build partial sentence hypothesis until total sentences are found. An original
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