Speaker-independent consonant classification in continuous speech with distinctive features and neural networks.

This paper provides experimental evidence to the assertion that the design of appropriate neural networks (NN) for speech recognition should be inspired by acoustic and phonetic knowledge, and not only by knowledge in pattern recognition. Rather than investigating the NN learning paradigm, the paper is focused on the influence of the input parameters, of the internal structure, and of the desired output representation on the classification performance of recurrent multilayer perceptrons. As an instructive example, the paper analyzes the problem of classifying ten stop and nasal consonants in continuous speech independently of the speaker. Experiments are reported for the TIMIT database, using 343 speakers in the training set and 77 different speakers in the test set. Comparative experiments show that good performance is obtained when many input acoustic parameters are used, including a time/frequency gradient operator related to transitions of the second formant, and when the desired outputs represent context-dependent articulatory features. Classification is performed by principal component analysis of the NN outputs. Refinement of the design parameters yield increasingly better performance on the test set, ranging from 45% errors for a perceptron without hidden nodes to 23.3% errors for the best NN.

Global optimization of a neural network-hidden Markov model hybrid.

The integration of multilayered and recurrent artificial neural networks (ANNs) with hidden Markov models (HMMs) is addressed. ANNs are suitable for approximating functions that compute new acoustic parameters, whereas HMMs have been proven successful at modeling the temporal structure of the speech signal. In the approach described, the ANN outputs constitute the sequence of observation vectors for the HMM. An algorithm is proposed for global optimization of all the parameters. Results on speaker-independent recognition experiments using this integrated ANN-HMM system on the TIMIT continuous speech database are reported.

Learning and plan refinement in a knowledge-based system for automatic speech recognition.

This paper shows how a semiautomatic design of a speech recognition system can be done as a planning activity. Recognition performances are used for deciding plan refinement. Inductive learning is performed for setting action preconditions. Experimental results in the recognition of connected letters spoken by 100 speakers are presented.

Parallel algorithms for syllable recognition in continuous speech.

A distributed rule-based system for automatic speech recognition is described. Acoustic property extraction and feature hypothesization are performed by the application of sequences of operators. These sequences, called plans, are executed by cooperative expert programs. Experimental results on the automatic segmentation and recognition of phrases, made of connected letters and digits, are described and discussed.

Use of fuzzy algorithms for phonetic and phonemic labeling of continuous speech.

A model for assigning phonetic and phonemic labels to speech segments is presented. The system executes fuzzy algorithms that assign degrees of worthiness to structured interpretations of syllabic segments extracted from the signal of a spoken sentence. The knowledge source is a series of syntactic rules whose syntactic categories are phonetic and phonemic features detected by a precategorical and a categorical classification of speech sounds. Rules inferred from experiments and results for male and female voices are presented.