Misarticulated /r/ – Speech Corpus and Automatic Recognition Technique
Suresh Kumar Nagaram1, Suman Maloji2, Kasiprasad Mannepalli3
1Suresh Kumar Nagaram, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation,Vaddeswaram, Guntur, Andhra Pradesh 522502, India.
2Suman Maloji, Professor, Department of Electronics and Computers Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh 522502, India.
3KasiprasadMannepalli, Associate Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh 522502, India.
Manuscript received on 13 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 172-177 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2164037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Technique to recognize the impaired pronunciation of sound /r/ from Telugu speech signals is presented in this paper besides the speech corpus. Rhotacism is called as an inability to pronounce the sound /r/ and is one of the Speech Sound Disorders (SSD) in children. Whose SSD not diagnosed at an early stage may result ina lack of social skills. This demands an efficient automatic speech impairment detection technique, which helps the therapists to treat the patients with impairment specific procedure. Databases for the impaired articulation of /r/ in various languages are explored in this article. The shape of the envelope, timbre, Walsh Hadamard Transform (WHT), Discreet Cosine Transform (DCT) features extracted, from the Mel-Frequency Cepstral Coefficients (MFCC), to discriminate the correct and wrong articulation of /r/ are detailed. Usage of kNearest Neighbor (kNN), Support Vector Machine (SVM) and kohonen neural networks in various articles, for classification, are briefed. MFCC features and k-NN algorithm is used to identify the misarticulation in the Telugu language. The 80.1% classification accuracy shows that the proposed method performs good with respect to the methods detailed for other languages. Availability of acoustic databases for the impaired articulation of /r/ and subjects with such impairment restricts the performance validation of the investigated methods. This further demand the more contribution from scholars in the development of automatic techniques and databases for misarticulated /r/ in different languages.
Keywords: Speech Sound Disorder, Rhotacism, Impaired Articulation, Impaired Speech, Dyslalia.
Scope of the Article: Image Processing and Pattern Recognition