Improved method of heuristic classification of vowels from an acoustic signal

This paper describes research in the field of the improved methodology of the classification of vowels /a, a:/, /ε , ε :/, /i, i:/, /o, o:/, and /u, u:/ (vowel symbols according to IPA, i.e. International Phonetic Alphabet). The aim is to develop an improved method enabling the automatic allocation of vowel symbols to the corresponding time segments of acoustic recordings of an undisturbed speech signal. The combined classification method is based on finding frequencies of the first two local maxims (formants) in a smoothed linear predictive amplitude spectrum (LPC, linear predictive coding) and zero-crossing values of each speech active voiced short-term segment of the recording. Based on these monitored values, simple heuristic conditions are arranged for the classification of the respective vowel. Implementation of the algorithm was realized using the MATLAB environment and its Graphical User Interface (GUI) was used for the user interaction. Verification of the success rate of vowel classification was done using recordings of forty speakers (twenty men and twenty women), where each speaker repeated the vowels repeatedly with short successive pauses. The success rate of recognizing vowels is classified and evaluated based on results obtained from our designed method.

[1]  R. Venkatesha Prasad,et al.  Comparison of voice activity detection algorithms for VoIP , 2002, Proceedings ISCC 2002 Seventh International Symposium on Computers and Communications.

[2]  Miroslav Stanek,et al.  Finding the Most Uniform Changes in Vowel Polygon Caused by Psychological Stress , 2015 .

[3]  Alex Acero,et al.  Spoken Language Processing: A Guide to Theory, Algorithm and System Development , 2001 .

[4]  Radek Martinek,et al.  Testing of the voice communication in smart home care , 2015, Human-centric Computing and Information Sciences.

[5]  Damian Campo,et al.  A novel emotion recognition technique from voiced-speech , 2017, 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC).

[6]  Radek Martinek,et al.  A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS , 2015 .

[7]  Kandarpa Kumar Sarma,et al.  Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework , 2012, Appl. Comput. Intell. Soft Comput..

[8]  Noureddine Ellouze,et al.  An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition , 2012, ArXiv.

[9]  Youngjik Lee Lee,et al.  Selecting Good Speech Features for Recognition , 1996 .

[10]  Ladislav Polak,et al.  Algorithms for vowel recognition in fluent speech based on formant positions , 2013, 2013 36th International Conference on Telecommunications and Signal Processing (TSP).

[11]  Miroslav Stanek,et al.  Speaker distinction using vowel polygons: Experimental study , 2015, 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA).

[12]  Dongsuk Yook,et al.  Robust Voice Activity Detection Using the Spectral Peaks of Vowel Sounds , 2009 .

[13]  Roel Smits,et al.  A comparison of vowel normalization procedures for language variation research. , 2004, The Journal of the Acoustical Society of America.

[14]  Stephen A. Zahorian,et al.  Formant estimation from cepstral coefficients using a feedforward memoryless neural network , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[15]  B. Lobanov Classification of Russian Vowels Spoken by Different Speakers , 1971 .

[16]  Radek Martinek,et al.  Adaptive noise suppression in voice communication using a neuro-fuzzy inference system , 2015, 2015 38th International Conference on Telecommunications and Signal Processing (TSP).

[17]  Qin Yan,et al.  Analysis and Synthesis of Formant Spaces of British, Australian, and American Accents , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[18]  Sandeep Kumar Performance Evaluation of Novel AMDF-Based Pitch Detection Scheme , 2016 .

[19]  Sang Jo Lee,et al.  Development of an Optimized Feature Extraction Algorithm for Throat Signal Analysis , 2007 .

[20]  Yunkeun Lee,et al.  Intra- and Inter-frame Features for Automatic Speech Recognition , 2014 .

[21]  Ronald W. Schafer,et al.  Introduction to Digital Speech Processing , 2007, Found. Trends Signal Process..

[22]  Shipra Mishra,et al.  Hindi vowel classification using QCN-MFCC features , 2016 .

[23]  Radek Martinek,et al.  Voice Control of Technical Functions in Smart Home with KNX Technology , 2015 .

[24]  Zdenek Machacek Analysis and Elimination of Dangerous Wave Propagation as Intelligent Adaptive Technique , 2011, ACIIDS.