Piotr Kłosowski

Biometric Voice Identification Based on Fuzzy Kernel Classifier

This paper presents research on automatic speaker identification based on structural risk minimization and kernel functions. New approach, known as a Fuzzy Kernel Ho-Kashyap classifier FKHK, to speaker identification was applied. Instead of the most popular kernel functions like gaussian or polynomial, data dependent kernel matrix which may be interpreted in terms of linguistic values from the premises of if-then rules was applied. Classifier was tested on polish speech corpora ROBOT and obtained results were discussed.

Speech Processing Application Based on Phonetics and Phonology of the Polish Language

The article presents methods of improving speech processing based on phonetics and phonology of Polish language. The new presented method for speech recognition was based on detection of distinctive acoustic parameters of phonemes in Polish language. Distinctivity has been assumed as the most important selection of parameters, which have represented objects from recognized classes. Speech recognition is widely used in telecommunications applications.

Polish semantic speech recognition expert system supporting electronic design system

The paper presents an EDA system, which is supported by the experimental speech recognition system menu with the dialog module. The main idea of the system is presented. Some aspects concerning the proposed speech recognition methodology are described. Problems specific to the Polish language are emphasized. The idea of dialog system menus is discussed. The inference engine based on AI techniques supporting Polish (natural) language processing is proposed. The entire expert system architecture is introduced. Implementation and examples are discussed.

Automatic Speech Segmentation for Automatic Speech Translation

The article presents selected, effective speech signal processing algorithms and their use in order to improve the automatic speech translation. Automatic speech translation uses natural language processing techniques implemented using algorithms of automatic speech recognition, speaker recognition, automatic text translation and text-to-speech synthesis. It is very possible to improve the process of automatic speech translation by using effective algorithms for automatic segmentation of speech signals based on speaker recognition and language recognition.

Speaker recognition system with good generalization properties

This paper presents speaker recognition system possessing very good generalization properties. Relatively low equal error rate for speaker verification and high identification rate for identification are achieved for very short training and testing sequences. This behaviour is achieved for the kernel modification of a classic Ho-Kashyap linear classifier. Achieved results for the new approach are compared with results for the classic GMM and VQ techniques. Speech of a moderately good quality from the Polish speech corpus was used for development of recognition system.

Influence of Feature Dimensionality and Model Complexity on Speaker Verification Performance

This paper provides description of a text dependent speaker recognition system based on vector quantization approach. The scope of this paper is to check influence of feature dimensionality and the complexity of the speaker model on verification process. Provided results show that MFCC features yield the lowest possible verification errors among all tested parameters. Although dimensionality of feature vectors is important, there is no need to increase it above some level as the improvement in verification performance is relatively low and computational complexity increases. Far more important than dimensionality is complexity of the speaker model.

Speech Recognition Based on Open Source Speech Processing Software

Creating of speech recognition application requires advanced speech processing techniques realized by specialized speech processing software. It is very possible to improve the speech recognition research by using frameworks based on open source speech processing software. The article presents the possibility of using open source speech processing software to construct own speech recognition application.

Influence of Corpus Size on Speaker Verification

The scope of this paper is to check influence of the size of the speech corpus on the speaker recognition performance. Obtained results for TIMIT corpus are compared with results obtained for smaller database ROBOT. Additionally influence of feature dimensionality and size of the speaker model was tested. Achieved results show that the best results can be obtained for MFCC features. The lowest EER for larger TIMIT database are 4 times worse than the best result for ROBOT corpus which confirms that biometric systems should be tested on as large data sets as possible to assure that achieved error rates are statistically significant.

Speaker Verification Performance Evaluation Based on Open Source Speech Processing Software and TIMIT Speech Corpus

Creating of speaker recognition application requires advanced speech processing techniques realized by specialized speech processing software. It is very possible to improve the speaker recognition research by using speech processing platform based on open source software. The article presents the example of using open source speech processing software to perform speaker verification experiments designed to test various speaker recognition models based on different scenarios. Speaker verification efficiency was evaluated for each scenario using TIMIT speech corpus distributed by Linguistic Data Consortium. The experiment results allowed to compare and select the best scenario to build speaker model for speaker verification application.

Algorithm and implementation of automatic phonemic transcription for Polish

The article presents rule-based automatic phonemic transcription method for Polish, implemented by the author in Python programming language. Automatic phonemic transcription application required: phonemic transcription rules formulation and implementation, and automatic phonemic transcription algorithm implementation. As the implementation result, automatic phonemic transcription application was developed by the author, which enables to execute automatic phonemic transcription of any orthographic text files in Polish. The use of large language corpus files, as source orthographic text input files for automatic phonemic transcription enables to create large phonemic language corpora for further speech and language processing research. The developed phonemic language corpus for Polish, opens up further opportunities to continue research on improving automatic speech recognition. The large phonemic language corpus enables to perform statistical analysis of the language and to develop statistical phonemic language models for improving automatic speech recognition by statistical methods.