John Sirigos

A hybrid syllable recognition system based on vowel spotting

In this paper we present a hybrid ANN/HMM syllable recognition system based on vowel spotting. Using an advanced multilevel vowel-spotting module we track all vowel phonemes in speech signals from where we model the speech segments located between two successive vowels which are defined as syllables. In order to achieve minimum vowel losses and accurate detection, we focus on taking special care of the vowel spotter which is based on three different techniques: discrete hidden Markov models (DHMMs), multilayer perceptrons and heuristic rules.To set up the models of the syllable segments, hybrid DHMMs with multiple codebooks are used. The usual DHMM probability parameters are replaced by combined neural network outputs. For this purpose, we use both context dependent and context independent neural networks.The syllable recognition system was tested with the TIMIT and NTIMIT databases and the results obtained showed 75.09% and 59.30% average syllable recognition accuracy, respectively. It has to be noted that to achieve the above results no grammars or syllable-based lexicons were used.

A high performance text independent speaker recognition system based on vowel spotting and neural nets

We present a text independent speaker recognition system based on vowel spotting and feed forward multilayer perceptrons (MLPs). The perceptual linear predictive (PLP) speech analysis technique was used for parameter estimation, a feed forward MLP for vowel spotting and a simple MLP for the classification procedure. To train and test the system we used the TIMIT database. We conclude with results of the speaker verification and identification process, showing that the system described has a high recognition accuracy (/spl sim/98%) using short test utterances (2.5 sec). It also has a real-time response, is easily adapted to new speakers and requires a small amount of data for training purposes (three sentences per speaker).

Improving environmental robustness of speech recognition using neural networks

This paper presents a method for improving speech recognition in noisy environment by using neural networks. Two multilayer perceptrons (MLPs) are used. The first MLP minimises the difference between noisy and clean speech and the second one measures the degree of noise in the speech signal and adjusts the time interval between subsequent frames of the processed speech signal accordingly. If we use the technique presented in this paper as a pre-processing stage of a speech recognition system we can extend the application of the system to different environments without re-training it. We need only to train the preprocessing stage with a small portion of noisy data which is created by conducting part of the original clean speech database used for training the speech recognizer through the desired environment. There is no need for creating a new database in the desired working environment. Our method was tested on a vowel spotting system, and is trained with two well known databases: TIMIT and NTIMIT. The evaluation of the system through a vowel spotting process, shows a significant improvement of the recognition rate of the system.

Vowel-non vowel decision using neural networks and rules

This paper describes a speaker independent vowel/non-vowel classifier based on neural networks and several rules. RASTA-PLP analysis of the speech signal resulting to mel-cepstral coefficients and a formant tracking method are used in order to provide the feature vectors for the MLP. To train and test the system we used a part of the TIMIT database. The results indicate that the performance of this classifier for speaker independent vowel classification is approximately 98.5% so it can be favorably used for speaker recognition or speech labeling purposes.