An efficient method for Tamil speech recognition using MFCC and DTW for mobile applications

Dalmiya, C. P.; Dharun, V. S.; Rajesh, K.P.

doi:10.1109/cict.2013.6558295

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…This method converts the global optimal problem to be solved into a local optimal problem at each step [5]. At present, the method based on DTW or DTW deformation (Segmenta-DTW) is still in the mainstream research ranks [6][7][8]. The method based on template matching has the advantages of small model size and less calculation, but the accuracy is not as good as the other two methods.…”

Section: Related Workmentioning

confidence: 99%

Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Yong-liang

2021

J. eng.

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Language keyword extraction has broad application prospects. Most current keyword search networks are based on speech recognition and text search. This method is helpless for some languages that are difficult to text and cannot locate the location where the keywords appear. A multi‐level structure of speech keyword location and recognition system (MSS‐Net), which can locate the location interval of keywords while recognizing keywords is proposed here. MSS‐Net contains three levels. The first level is the voice start and end detection module, which is responsible for detecting the start and end positions of valid audio. When valid audio is detected, the second level is activated: the keyword detection module. The keyword detection module detects keyword information in the audio signal and locates the keyword position interval. The third level is the keyword recognition module. The system recognizes the detected position interval of the location to obtain specific keyword information. Comparative experiments show that MSS‐Net is more accurate for keyword positioning, with a network recall rate of 94.91% and an accuracy rate of 96.32%, which is better than other network models participating in the experiment. In addition, the network has strong anti‐interference ability, which can achieve good suppression effect for normal life noise. The ablation experiment verified the effect of the hierarchical network structure on the performance of speech recognition tasks.

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Section: Related Workmentioning

confidence: 99%

Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Yong-liang

2021

J. eng.

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“…Two filter types exist in MFCC which are linearly set apart at low frequency below 1kHz and a logarithm spacing above 1kHz [89], [90]. Generally, feature extractions using MFCC involve the following steps; Preemphasis, framing, hamming windowing, Fast Fourier Transform (FFT), the Mel-scale Filter bank, Logarithm operation and Discrete Cosine Transform (DCT) as explicitly explained in [87], [90], [91]. A block diagram of these steps for extracting features using MFCC technique is shown in The corresponding value for frequency f is expressed in Hz and the i th mel-ceptral coefficient is shown in Equations (12) and (13) respectively, where K is the total number of cepstral coefficients, X k is the logarithmic energy of the kth mel-spectrum band.…”

Section: F Mel-scale Frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

2020

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Cetaceans have elicited the attention of researchers in recent decades due to their importance to the ecosystem and their economic values. They use sound for communication, echolocation and other social activities. Their sounds are highly non-stationary, transitory and range from short to long sounds. Passive acoustic monitoring (PAM) is a popular method used for monitoring cetaceans in their ecosystems. The volumes of data accumulated using PAM are usually big, so they are difficult to analyze using manual inspection. Therefore different techniques with mixed outcomes have been developed for the automatic detection and classification of signals of different cetacean species. So far, no single technique developed is perfect to detect and classify the vocalizations of over 82 known species due to variability in time-frequency, difference in the amplitude among species and within species' vocal repertoire, physical environment, among others. The accuracy of any detector or classifier depends on the technique adopted as well as the nature of the signal to be analyzed. In this article, we review the existing techniques for the automatic detection and classification of cetacean vocalizations. We categorize the surveyed techniques, while emphasizing the advantages and disadvantages of these techniques. The article suggests possible research directions that can improve existing detection and classification techniques. In addition, the article recommends other suitable techniques that can be used to analyze non-linear and non-stationary signals such as the cetaceans' signals. Several research have been dedicated to this topic, however, there is no review of these past results that gives a quick overview in the area of cetacean detection and classification. This review will help researchers and practitioners in the field to make insightful decisions based on their requirements.

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“…Keluaran HMM adalah sekuens dari simbol atau kuantitas, dimana sebuah sinyal dari pengucapan bisa dilihat seperti piecewise stationary signal atau short-time stationary signal [2]. HMM dikenal dapat diujikan secara otomatis, sederhana dan secara komputasi layak untuk digunakan [3], [4].…”

Section: Pendahuluanunclassified

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

Jollyta

Oktarina

Johan³

2020

JEPIN

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Teknologi pengenal suara (speech recognition) merupakan teknologi yang berkembang pesat dalam bidang kecerdasan buatan (artificial intelligent). Saat ini, teknologi pengenal suara menjadi hal yang komersil melalui berbagai media teknologi seperti smartphone dan komputer. Salah satu pembentuk struktur pengenal suara agar dapat bekerja pada perangkat tersebut adalah model statistik pengenal suara Hidden Markov Model (HMM). Penerapan HMM pada berbagai kasus menunjukkan bahwa model ini cocok dengan berbagai macam data. Tulisan ini merupakan sebuah tinjauan untuk model HMM yang bertujuan untuk memberikan gambaran dan pemahaman terhadap kinerja HMM melalui rangkuman sejumlah penelitian yang digunakan dalam berbagai data. Penerapan HMM tersebut menunjukkan optimalisasi kinerja HMM dan tinjauan terhadap sejumlah penelitian menunjukkan bahwa tingkat keberhasilan HMM dalam mengenali data mencapai 71.43%.

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An efficient method for Tamil speech recognition using MFCC and DTW for mobile applications

Cited by 16 publications

References 5 publications

Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

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