MFCC based recognition of repetitions and prolongations in stuttered speech using k-NN and LDA

Chee, Lim Sin; Ooi, Chia Ai; Hariharan, M.; Yaacob, Sazali

doi:10.1109/scored.2009.5443210

Cited by 53 publications

(14 citation statements)

References 7 publications

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“…Three different classifiers (k-NN, LDA, and SVM) were employed and compared in the study. [2,3] using K-NN and LDA classifier using the MFCC and linear prediction cepstral coefficients (LPCC) features. Three feature extraction methods such as the LPC, LPCC, and weighted linear prediction cepstral coefficients (WLPCCs) were proposed by Hariharan et al [11].…”

Section: Related Workmentioning

confidence: 99%

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

Mahesha¹,

Vinod²

2016

Journal of Intelligent Systems

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The classification of dysfluencies is one of the important steps in objective measurement of stuttering disorder. In this work, the focus is on investigating the applicability of automatic speaker recognition (ASR) method for stuttering dysfluency recognition. The system designed for this particular task relies on the Gaussian mixture model (GMM), which is the most widely used probabilistic modeling technique in ASR. The GMM parameters are estimated from Mel frequency cepstral coefficients (MFCCs). This statistical speakermodeling technique represents the fundamental characteristic sounds of speech signal. Using this model, we build a dysfluency recognizer that is capable of recognizing dysfluencies irrespective of a person as well as what is being said. The performance of the system is evaluated for different types of dysfluencies such as syllable repetition, word repetition, prolongation, and interjection using speech samples from the University College London Archive of Stuttered Speech (UCLASS).

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Section: Related Workmentioning

confidence: 99%

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

Mahesha¹,

Vinod²

2016

Journal of Intelligent Systems

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“…On the other hand, the LPC synthesis model consists of an excitation source ( ), which provides input to the spectral shaping filter ( ), which will give the synthesized output spe-ech ( ) as given by equation (6) [14]:…”

Section: Speech Synthesis Filtermentioning

confidence: 99%

“…Stuttering can also be defined as a disruption in the normal flow of speech unintentionally by dysfluencies, which include repetitive pronunciation, prolonged pronunciation, blocked or stalled pronunciation at the phoneme or the syllable level [6][7][8]. Stuttering cannot be permanently cured, however, it may go into remission after some time, or stutterers can learn to shape their speech into fluent speech with the appropriate speech pathology treatment.…”

Section: Introductionmentioning

confidence: 99%

“…This shaping has its effects on the tempo, loudness, effort, or duration of their utterances [7,9]. Stuttering has been found to be more prevalent in males than females (ratio 4:1) [1,2,6,9,10]. Stutterers and non-stutterers alike have speech dysfluencies, which are gaffes or disturbances in the flow of words a speaker plans to say, but dysfluencies are more observable in stutterers' speech [11].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Stuttered Speech Correction

Alim

Rashid

Sediono

et al. 2016

Jurnal Ilmu Komputer dan Informasi

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Stuttered speech is a dysfluency rich speech, more prevalent in males than females. It has been associated with insufficient air pressure or poor articulation, even though the root causes are more complex. The primary features include prolonged speech and repetitive speech, while some of its secondary features include, anxiety, fear, and shame. This study used LPC analysis and synthesis algorithms to reconstruct the stuttered speech. The results were evaluated using cepstral distance, Itakura-Saito distance, mean square error, and likelihood ratio. These measures implied perfect speech reconstruction quality. ASR was used for further testing, and the results showed that all the reconstructed speech samples were perfectly recognized while only three samples of the original speech were perfectly recognized. Keywords: stuttered speech, speech reconstruction, LPC analysis, LPC synthesis, objective quality measure AbstrakShuttered speech adalah speech yang kaya dysfluency, lebih banyak terjadi pada laki-laki daripada perempuan. Ini terkait dengan tekanan udara yang tidak cukup atau artikulasi yang buruk, meskipun akar penyebabnya lebih kompleks. Fitur utama termasuk speech yang berkepanjangan dan berulangulang, sementara beberapa fitur sekunder meliputi, kecemasan, ketakutan, dan rasa malu. Penelitian ini menggunakan LPC analysis dan synthesis algoritma untuk merekonstruksi stuttered speech. Hasil dievaluasi menggunakan jarak cepstral, jarak Itakura-Saito, mean square error, dan rasio likelihood. Langkah-langkah ini terkandung kualitas speech reconstruction yang sempurna. ASR digunakan untuk pengujian lebih lanjut, dan hasilnya menunjukkan bahwa semua sampel speech yang terekonstruksi dikenali dengan sempurna sementara hanya tiga sampel dari speech asli dikenali dengan sempurna.

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“…In particular, Mel frequency Cepstral coefficients are one of the most employed [5]. Yet, these previously mentioned methods require linearity [6], and it has been proved that nonlinear techniques can extract more information in voice signals than linear techniques [7].…”

Section: Introductionmentioning

confidence: 99%

Complex networks: Application to pathology detection in voice signals

Hurtado-Jaramillo

Guarín

Orozco

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The method of complex networks has been proposed as a novel approach to analyze time series from a new perspective. However, only few studies have applied this methodology to certain types of pseudo-periodic signals. In this article, the network-based technique is applied on voice signals, a kind of pseudo-periodic signals which has not been analyzed using complex networks, to differentiate between a healthy subject and subjects with pathological disorders. The results obtained demonstrated that through a set of statistic computed from the complex networks is possible to differentiate between healthy and non-healthy subjects, contrary to what was observed using well known non-linear statistics, such as Lempel-Ziv complexity and sample entropy. We conclude that by seeing voice signals as complex networks new information can be extracted from the time series that may help in the diagnosis of pathologies.

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MFCC based recognition of repetitions and prolongations in stuttered speech using k-NN and LDA

Cited by 53 publications

References 7 publications

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

A Novel Approach to Stuttered Speech Correction

Complex networks: Application to pathology detection in voice signals

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